Neural Networks MCQs with Answers

Introduction :

Dive into the core of Artificial Intelligence with our extensive collection of Neural Networks Multiple Choice Questions (MCQs) and detailed answers! Neural Networks are the backbone of modern AI, driving advancements in fields like computer vision, natural language processing, and robotics. This resource is meticulously designed for students, AI engineers, and enthusiasts in India and globally who are eager to solidify their understanding of fundamental concepts, including neuron structure, activation functions, feedforward networks, backpropagation, and specialized architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Whether you're preparing for a challenging AI interview, studying for an academic examination, or simply aiming to deepen your knowledge, our carefully curated MCQs offer an excellent and practical way to test and enhance your expertise.


1 .
Who is introduced Neural Networks?
A)
Claude Shannon
B)
Vannevar Bush
C)
Warren Sturgis McCulloch
D)
John von Neumann

Correct Answer :   Warren Sturgis McCulloch

Explanation : Neural networks were first proposed in 1944 by Warren Sturgis McCulloch and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what's sometimes called the first cognitive science department.

2 .
Why do we need biological neural networks?
A)
to make smart human interactive & user friendly system
B)
to apply heuristic search methods to find solutions of problem
C)
to solve tasks like machine vision & natural language processing
D)
All of the Above

Correct Answer :   All of the Above

Explanation : These are the basic aims that a neural network achieve.

3 .
What is the trend in software nowadays?
A)
to be versatile
B)
to be task specific
C)
to solve complex problems
D)
to bring computer more & more closer to user

Correct Answer :   to bring computer more & more closer to user

Explanation : Software should be more interactive to the user, so that it can understand its problem in a better fashion.

4 .
Operations in the neural networks can perform what kind of operations?
A)
Serial
B)
Serial or parallel
C)
Parallel
D)
None of the Above

Correct Answer :   serial or parallel

Explanation : General characteristics of neural networks.

5 .
Which action is faster pattern classification or adjustment of weights in neural nets?
A)
pattern classification
B)
equal
C)
adjustment of weights
D)
either of them can be fast, depending on conditions

Correct Answer :   pattern classification

Explanation : Memory is addressable, so thus pattern can be easily classified.

6 .
What is the feature of ANNs due to which they can deal with noisy, fuzzy, inconsistent data?
A)
distributive nature of networks
B)
associative nature of networks
C)
both associative & distributive
D)
None of the Above

Correct Answer :   both associative & distributive

7 .
What was the name of the first model which can perform weighted sum of inputs?
A)
Hopfield model of neuron
B)
McCulloch-pitts neuron model
C)
Marvin Minsky neuron model
D)
None of the Above

Correct Answer :   McCulloch-pitts neuron model

Explanation : McCulloch-pitts neuron model can perform weighted sum of inputs followed by threshold logic operation.

8 .
What's the main point of difference between human & machine intelligence?
A)
human have emotions
B)
human have sense organs
C)
human have more IQ & intellect
D)
human perceive everything as a pattern while machine perceive it merely as data

Correct Answer :   human perceive everything as a pattern while machine perceive it merely as data

Explanation : Humans have emotions & thus form different patterns on that basis, while a machine(say computer) is dumb & everything is just a data for him.

9 .
What is auto-association task in neural networks?
A)
inputs
B)
predicting the future inputs
C)
related to storage & recall task
D)
find relation between 2 consecutive inputs

Correct Answer :   related to storage & recall task

Explanation : This is the basic definition of auto-association in neural networks.

10 .
What is adaline in neural networks?
A)
adaptive linear element
B)
adaptive line element
C)
automatic linear element
D)
None of the Above

Correct Answer :   adaptive linear element

11 .
What are models in neural networks?
A)
Representation of biological neural networks
B)
Mathematical representation of our understanding
C)
Both First & Second
D)
None of the Above

Correct Answer :   Both First & Second

12 .
How many types of Artificial Neural Networks?
A)
1
B)
2
C)
3
D)
4

Correct Answer :   2

13 .
What does RNN Stands for?
A)
Recurrent Neural Network
B)
Recurring Neural Network
C)
Removable Neural Network
D)
None of the Above

Correct Answer :   Recurrent Neural Network

14 .
What is unsupervised learning?
A)
Features of group explicitly stated
B)
Neither feature & nor number of groups is known
C)
Number of groups may be known
D)
None of the above

Correct Answer :   Neither feature & nor number of groups is known

15 .
Example of a unsupervised feature map?
A)
Text recognition
B)
Voice recognition
C)
Image recognition
D)
None of the Above

Correct Answer :   Voice recognition

Explanation : Since same vowel may occur in different context & its features vary over overlapping regions of different vowels.

16 .
Drawbacks of template matching are?
A)
More generalized
B)
Time consuming
C)
Highly restricted
D)
None of the Above

Correct Answer :   Highly restricted

Explanation : Point to point pattern matching is carried out in the process.

17 .
What is plasticity in neural networks?
A)
Output is static
B)
Input pattern has become static
C)
Output pattern keeps on changing
D)
Input pattern keeps on changing

Correct Answer :   Input pattern keeps on changing

Explanation : Dynamic nature of input patterns in an AI(Artificial Intelligence) problem.

18 .
What is stability plasticity dilemma?
A)
Static inputs
B)
Static inputs & categorization can’t be handled
C)
System can neither be stable nor plastic
D)
Dynamic inputs & categorization can’t be handled

Correct Answer :   Dynamic inputs & categorization can’t be handled

Explanation : If system is allowed to change its categorization according to inputs it cannot be used for patterns classification & assessment.

19 .
What is the contribution of Ackley, Hinton in neural?
A)
Boltzman machine
B)
Perceptron
C)
Learning algorithms
D)
None of the Above

Correct Answer :   Boltzman machine

Explanation : Ackley, Hinton built the Boltzman machine.

20 .
Who developed the first learning machine in which connection strengths could be adapted automatically?
A)
Hopfield
B)
Marvin Minsky
C)
McCulloch-pitts
D)
None of the Above

Correct Answer :   Marvin Minsky

Explanation : In 1954 Marvin Minsky developed the first learning machine in which connection strengths could be adapted automatically & efficiebtly.

21 .
Who proposed the first perceptron model in 1958?
A)
Hopfield
B)
Marvin Minsky
C)
Rosenblatt
D)
McCulloch-pitts

Correct Answer :   Rosenblatt

Explanation : Rosenblatt proposed the first perceptron model in 1958 .

22 .
John hopfield was credited for what important aspec of neuron?
A)
Energy analysis
B)
Learning algorithms
C)
Adaptive signal processing
D)
None of the Above

Correct Answer :   Energy analysis

Explanation : It was of major contribution of his works in 1982.

23 .
What are the issues on which biological networks proves to be superior than AI networks?
A)
Flexibility
B)
Collective computation
C)
Robustness & fault tolerance
D)
All of the Above

Correct Answer :   All of the Above

Explanation : AI network should be all of the above mentioned.

24 .
The fundamental unit of network is
A)
Axon
B)
Neuron
C)
Brain
D)
Nucleus

Correct Answer :   Neuron

Explanation : Neuron is the most basic & fundamental unit of a network .

25 .
What are dendrites?
A)
Fibers of nerves
B)
Nuclear projections
C)
Other name for nucleus
D)
None of the Above

Correct Answer :   Fibers of nerves

26 .
What is shape of dendrites like
A)
oval
B)
round
C)
tree
D)
rectangular

Correct Answer :   tree

27 .
Signal transmission at synapse is a?
A)
Physical Process
B)
Chemical Process
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   Chemical Process

28 .
What is approx size of neuron body(in micrometer)?
A)
Below 5
B)
5-10
C)
10-80
D)
Above 100

Correct Answer :   10-80

Explanation : Average size of neuron body lies in the above limit.

29 .
What is the gap at synapses(in nanometer)?
A)
50
B)
100
C)
150
D)
200

Correct Answer :   200

30 .
What is charge at protoplasm in state of inactivity?
A)
Negative
B)
Neutral
C)
Positive
D)
May be positive or negative

Correct Answer :   Negative

Explanation : It is due to the presence of potassium ion on outer surface in neural fluid.

31 .
What is the main constituent of neural liquid?
A)
Iron
B)
Sodium
C)
Potassium
D)
None of the Above

Correct Answer :   Potassium

Explanation : Potassium is the main constituent of neural liquid & responsible for potential on neuron body.

32 .
What is average potential of neural liquid in inactive state?
A)
+70mv
B)
-70mv
C)
+35mv
D)
-35mv

Correct Answer :   -70mv

Explanation : It is a basic fact, founded out by series of experiments conducted by neural scientist.

33 .
How fast is propagation of discharge signal in cells of human brain?
A)
2-5m/s
B)
0.5-2m/s
C)
5-10m/s
D)
None of the Above

Correct Answer :   0.5-2m/s

Explanation : The process is very fast but comparable to the length of neuron.

34 .
The membrane which allows neural liquid to flow will?
A)
Regenerate & retain its original capacity
B)
Never be imperturbable to neural liquid
C)
Only the certain part get affected, while rest becomes imperturbable again
D)
None of the Above

Correct Answer :   Regenerate & retain its original capacity

35 .
At what potential does cell membrane looses it impermeability against Na+ ions?
A)
-50mv
B)
-35mv
C)
-65mv
D)
-60mv

Correct Answer :   -60mv

Explanation : Cell membrane looses it impermeability against Na+ ions at -60mv.

36 .
What is purpose of Axon?
A)
receptors
B)
transmitter
C)
transmission
D)
None of the Above

Correct Answer :   transmission

Explanation : Axon is the body of neuron & thus cant be at ends of it so cant receive & transmit signals.

37 .
The cell body of neuron can be analogous to what mathamatical operation?
A)
Integrator
B)
Differentiator
C)
Summing
D)
None of the Above

Correct Answer :   Summing

Explanation : Because adding of potential(due to neural fluid) at different parts of neuron is the reason of its firing.

38 .
What is the critical threshold voltage value at which neuron get fired?
A)
20mv
B)
10mv
C)
15mv
D)
30mv

Correct Answer :   10mv

Explanation : This critical is founded by series of experiments conducted by neural scientist.

39 .
What is name of above mechanism?
A)
Hebb rule learning
B)
Memory based learning
C)
Error correction learning
D)
None of the Above

Correct Answer :   Hebb rule learning

40 .
What is hebb's rule of learning
A)
The system learns from its past mistakes
B)
The strength of neural connection get modified accordingly
C)
The system recalls previous reference inputs & respective ideal outputs
D)
None of the Above

Correct Answer :   The strength of neural connection get modified accordingly

Explanation : The strength of neuron to fire in future increases, if it is fired repeatedly.

41 .
What is estimate number of neurons in human cortex?
A)
10³
B)
10⁵
C)
10⁸
D)
10¹¹

Correct Answer :   10¹¹

42 .
what is estimated density of neuron per mm^2 of cortex?
A)
5*(10⁴)
B)
15*(10²)
C)
15*(10³)
D)
15*(10⁴)

Correct Answer :   15*(10⁴)

43 .
Why can't we design a perfect neural network?
A)
Number of neuron is itself not precisely known
B)
Full operation is still not known of biological neurons
C)
Number of interconnection is very large & is very complex
D)
All of the Above

Correct Answer :   All of the Above

Explanation : These are all fundamental reasons, why can’t we design a perfect neural network !

44 .
How many synaptic connection are there in human brain?
A)
10¹⁵
B)
10¹⁰
C)
10⁵
D)
10²⁰

Correct Answer :   10¹⁵

Explanation : You can estimate this value from number of neurons in human cortex & their density.

45 .
What is ART in neural networks?
A)
Artificial resonance theory
B)
Adaptive resonance theory
C)
Automatic resonance theory
D)
None of the Above

Correct Answer :   Adaptive resonance theory

46 .
Positive sign of weight indicates?
A)
Inhibitory input
B)
Can be either excitatory or inhibitory as such
C)
Excitatory input
D)
None of the Above

Correct Answer :   Excitatory input

Explanation : Sign convention of neuron.

47 .
Negative sign of weight indicates?
A)
Excitatory input
B)
Excitatory output
C)
Inhibitory input
D)
Inhibitory output

Correct Answer :   Inhibitory input

Explanation : Sign convention of neuron.

48 .
The amount of output of one unit received by another unit depends on what?
A)
Weight
B)
Input unit
C)
Output unit
D)
Activation value

Correct Answer :   Weight

Explanation : Activation is sum of wieghted sum of inputs, which gives desired output..hence output depends on weights.

49 .
Who invented perceptron Neural Networks?
A)
Widrow
B)
Minsky & papert
C)
McCullocch-pitts
D)
Rosenblatt

Correct Answer :   Rosenblatt

Explanation : The perceptron is one of the earliest neural networks. Invented at the Cornell Aeronautical Laboratory in 1957 by Frank Rosenblatt, the Perceptron was an attempt to understand human memory, learning, and cognitive processes.

50 .
What was the 2nd stage in perceptron model called?
A)
Output unit
B)
Association unit
C)
Summing unit
D)
Sensory units

Correct Answer :   Association unit

Explanation : This was the very speciality of the perceptron model, that is performs association mapping on outputs of he sensory units.

51 .
What was the main deviation in perceptron model from that of MP model?
A)
Learning enabled
B)
More inputs can be incorporated
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   Learning enabled

52 .
What is delta (error) in perceptron model of neuron?
A)
Difference between desired & target output
B)
Can be both due to difference in target output or environmental condition
C)
Error due to environmental condition
D)
None of the Above

Correct Answer :   Error due to environmental condition

Explanation : All other parameters are assumed to be null while calculatin the error in perceptron model & only difference between desired & target output is taken into account.

53 .
How can output be updated in neural network?
A)
Synchronously
B)
Asynchronously
C)
Both Synchronously & Asynchronously
D)
None of the above

Correct Answer :   Both Synchronously & Asynchronously

Explanation : Output can be updated at same time or at different time in the networks.

54 .
What is asynchronous update in neural networks?
A)
Output units are updated in parallel fashion
B)
Output units are updated sequentially
C)
Can be either sequentially or in parallel fashion
D)
None of the Above

Correct Answer :   Output units are updated sequentially

Explanation : Output are updated at different time in the networks.

55 .
The procedure to incrementally update each of weights in neural is referred to as?
A)
Learning law
B)
Synchronisation
C)
Learning algorithm
D)
Both learning algorithm & law

Correct Answer :   Both learning algorithm & law

Explanation : Basic definition of learning law in neural.

56 .
Who invented the adaline neural model?
A)
Widrow
B)
Werbos
C)
Hopfield
D)
Rosenblatt

Correct Answer :   Widrow

Explanation : Widrow invented the adaline neural model.

57 .
What was the main point of difference between the adaline & perceptron model?
A)
Weights are compared with output
B)
Sensory units result is compared with output
C)
Analog activation value is compared with output
D)
All of the Above

Correct Answer :   Analog activation value is compared with output

Explanation : Analog activation value comparison with output, instead of desired output as in perceptron model was the main point of difference between the adaline & perceptron model.

58 .
What is the another name of weight update rule in adaline model based on its functionality?
A)
LMS error learning law
B)
Gradient descent algorithm
C)
Both LMS error & Gradient descent learning law
D)
None of the Above

Correct Answer :   Both LMS error & Gradient descent learning law

Explanation : weight update rule minimizes the mean squared error(delta square), averaged over all inputs & this laws is derived using negative gradient of error surface weight space.

59 .
In neural how can connections between different layers be achieved?
A)
Interlayer
B)
Intralayer
C)
Both Interlayer and Intralayer
D)
None of the Above

Correct Answer :   Both Interlayer and Intralayer

Explanation : Connections between layers can be made to one unit to another and within the units of a layer.

60 .
Connections across the layers in standard topologies & among the units within a layer can be organized?
A)
In feedback manner
B)
In feedforward manner
C)
Both feedforward & feedback
D)
Either feedforward & feedback

Correct Answer :   Either feedforward & feedback

Explanation : Connections across the layers in standard topologies can be in feedforward manner or in feedback manner but not both.

61 .
What is an instar topology?
A)
when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
B)
when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
C)
Can be either way
D)
None of the Above

Correct Answer :   when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent

Explanation : Restatement of basic definition of instar.

62 .
What is an outstar topology?
A)
when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
B)
when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
C)
Can be either way
D)
None of the Above

Correct Answer :   when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)

Explanation : Restatement of basic definition of outstar.

63 .
What is STM in neural network?
A)
Short Term Memory
B)
Short Topology Memory
C)
Stimulated Topology Memory
D)
None of the Above

Correct Answer :   Short Term Memory

Explanation : Full form of Short Term Memory(STM).

64 .
What does STM corresponds to?
A)
Either way
B)
Encoded pattern information pattern in synaptic weights
C)
Activation state of network
D)
All of the Above

Correct Answer :   Activation state of network

65 .
What LTM corresponds to?
A)
Either way
B)
Activation state of network
C)
Both (A) and (B)
D)
Encoded pattern information pattern in synaptic weights

Correct Answer :   Encoded pattern information pattern in synaptic weights

Explanation : Long-Term Memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time) & hence the option.

66 .
On what parameters can change in weight vector depend?
A)
input vector
B)
learning signal
C)
learning parameters
D)
All of the Above

Correct Answer :   All of the Above

Explanation : Change in weight vector corresponding to jth input at time (t+1) depends on all of these parameters.

67 .
Hebb's law can be represented by equation?
A)
∆wij= µf(wi a)aj
B)
∆wij= µ(si) aj, where (si) is output signal of ith input
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   Both (A) and (B)

Explanation : (si)= f(wi a), in Hebb’s law

68 .
widrow & hoff learning law is special case of?
A)
hebb learning law
B)
delta learning law
C)
perceptron learning law
D)
none of the above

Correct Answer :   delta learning law

Explanation : Output function in this law is assumed to be linear , all other things same.

69 .
What's the other name of widrow & hoff learning law?
A)
LMS
B)
MMS
C)
Hebb
D)
None of the Above

Correct Answer :   LMS

Explanation : LMS, least mean square. Change in weight is made proportional to negative gradient of error & due to linearity of output function.

70 .
Which of the following equation represent perceptron learning law?
A)
∆wij= µ(si) aj
B)
∆wij= µ(bi – si) aj
C)
∆wij= µ(bi – (wi a)) aj
D)
∆wij= µ(bi – si) aj Á(xi),wher Á(xi) is derivative of xi

Correct Answer :   ∆wij= µ(bi – si) aj

Explanation : Perceptron learning law is supervised, nonlinear type of learning.

71 .
Correlation learning law is what type of learning?
A)
Supervised
B)
Unsupervised
C)
Both supervised or unsupervised
D)
Either supervised or unsupervised

Correct Answer :   Supervised

Explanation : Supervised, since depends on target output.

72 .
The instar learning law can be represented by equation?
A)
∆wij= µ(si) aj
B)
∆wij= µ(bi – si) aj
C)
∆wij= µ(bi – si) aj Á(xi),where Á(xi) is derivative of xi
D)
∆wk= µ (a-wk), unit k with maximum output is identified

Correct Answer :   ∆wk= µ (a-wk), unit k with maximum output is identified

Explanation : Follows from basic definition of instar learning law.

73 .
Weight state i.e set of weight values are determined by what kind of dynamics?
A)
neural level dynamics
B)
synaptic dynamics
C)
can be either neural or synaptic dynamics
D)
None of the Above

Correct Answer :   synaptic dynamics

Explanation : Weights are best determined by synaptic dynamics, as it is one fastest & precise dynamics occurring.

74 .
Which is faster neural level dynamics or synaptic dynamics?
A)
Synaptic
B)
Insufficient information
C)
Neural level
D)
None of the Above

Correct Answer :   Neural level

75 .
Activation dynamics is referred as?
A)
short term memory
B)
long term memory
C)
either short or long term
D)
both short & long term

Correct Answer :   short term memory

Explanation : It depends on input pattern, & input changes from moment to moment, hence Short term memory.

76 .
What is generalization in Neural Networks?
A)
the ability of a pattern recognition system to approximate the desired output values for pattern vectors which are not in the test set.
B)
the ability of a pattern recognition system to approximate the desired output values for pattern vectors which are not in the training set.
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   the ability of a pattern recognition system to approximate the desired output values for pattern vectors which are not in the training set.

77 .
Changing inputs affects what kind of dynamics directly?
A)
neural
B)
synaptic
C)
activation
D)
both synaptic & neural

Correct Answer :   activation

Explanation : Activation dynamics depends on input pattern, hence any change in input pattern will affect activation dynamics of neural networks.

78 .
What's the actual reason behind the boundedness of the output function in activation dynamics?
A)
Limited neural fluid
B)
Limited fan in capacity of inputs
C)
Both limited neural fluid & fan in capacity
D)
None of the Above

Correct Answer :   None of the Above

Explanation : It is due to the limited current carrying capacity of cell membrane.

79 .
What is noise saturation dilemma?
A)
At saturation state neuron will stop working, while biologically it’s not feasible
B)
How can a neuron with limited operating range be made sensitive to nearly unlimited range of inputs
C)
Can be either way
D)
None of the Above

Correct Answer :   How can a neuron with limited operating range be made sensitive to nearly unlimited range of inputs

Explanation : Threshold value setting has to be adjusted properly.

80 .
Broadly how many kinds of stability can be defined in neural networks?
A)
2
B)
3
C)
4
D)
5

Correct Answer :   2

Explanation : There exist broadly structural & global stability in neural.

81 .
What is structural stability?
A)
When only synaptic dynamics in equilibrium
B)
When only synaptic dynamics in equilibrium
C)
When both synaptic & activation dynamics are simultaneously used & are in equilibrium
D)
None of the Above

Correct Answer :   None of the Above

Explanation : Refers to state equilibrium situation where small perturbations brings network back to equilibrium.

82 .
What is global stability?
A)
When only synaptic dynamics in equilibrium
B)
When only synaptic & activation dynamics are used
C)
When both synaptic & activation dynamics are simultaneously used & are in equilibrium
D)
None of the Above

Correct Answer :   When both synaptic & activation dynamics are simultaneously used & are in equilibrium

Explanation : Global stability means neuron as a whole is stable.

83 .
What was the goal of shunting activation model?
A)
To make system static
B)
To make system dynamic
C)
To keep operating range of activation value to a specified range
D)
None of the Above

Correct Answer :   To keep operating range of activation value to a specified range

Explanation : Stabilizing & bounding the unbounded range of activation value was the primary goal of this model.

84 .
If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?
A)
xb(t)=deterministic model
B)
xb(t)=deterministic model*noise component
C)
Both (A) and (B)
D)
xb(t)=deterministic model + noise component

Correct Answer :   xb(t)=deterministic model + noise component

Explanation : Noise is assumed to be additive in nature in stochastic models.

85 .
What is equilibrium in neural systems?
A)
Change in state, when small perturbations occur
B)
Settlement of network, when small perturbations occur
C)
Deviation in present state, when small perturbations occur
D)
None of the Above

Correct Answer :   Settlement of network, when small perturbations occur

Explanation : Follows from basic definition of equilibrium.

86 .
What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?
A)
xb(t)=0
B)
xb(t)=1
C)
xb(t)=n(t)+1
D)
xb(t)=n(t), where n is noise component

Correct Answer :   xb(t)=n(t), where n is noise component

Explanation : xb(t)=0 is condition for deterministic models, so option c is radical choice.

87 .
What is asynchronous update in a network?
A)
Update to all units is done at the same time
B)
Change in state of any one unit drive the whole network
C)
Change in state of any number of units drive the whole network
D)
None of the Above

Correct Answer :   Update to all units is done at the same time

Explanation : In asynchronous update, change in state of any one unit drive the whole network.

88 .
What are the requirements of learning laws?
A)
Convergence of weights
B)
Learning should use only local weights
C)
Learning time should be as small as possible
D)
All of the Above

Correct Answer :   All of the Above

Explanation : These all are the some of basic requirements of learning laws.

89 .
Memory decay affects what kind of memory?
A)
Memory
B)
Older memory in general
C)
Short term memory in general
D)
None of the Above

Correct Answer :   Short term memory in general

Explanation : Memory decay affects short term memory rather than older memories.

90 .
How is pattern information distributed?
A)
it is distributed in localised weights
B)
it is distributed in certain proctive weights only
C)
it is distributed all across the weights
D)
All of the Above

Correct Answer :   it is distributed all across the weights

Explanation : pattern information is highly distributed all across the weights.

91 .
What is hebbian learning?
A)
synaptic strength is proportional to correlation between firing of presynaptic neuron only
B)
synaptic strength is proportional to correlation between firing of post & presynaptic neuron
C)
synaptic strength is proportional to correlation between firing of postsynaptic neuron only
D)
None of the Above

Correct Answer :   synaptic strength is proportional to correlation between firing of post & presynaptic neuron

92 .
What is competitive learning?
A)
learning laws which modulate difference between synaptic weight & output signal
B)
learning laws which modulate difference between actual output & desired output
C)
learning laws which modulate difference between synaptic weight & activation value
D)
None of the Above

Correct Answer :   learning laws which modulate difference between synaptic weight & output signal

Explanation : Competitive learning laws modulate difference between synaptic weight & output signal.

93 .
What is differential hebbian learning?
A)
synaptic strength is proportional to correlation between firing of postsynaptic neuron only
B)
synaptic strength is proportional to correlation between firing of presynaptic neuron only
C)
synaptic strength is proportional to correlation between firing of post & presynaptic neuron
D)
synaptic strength is proportional to changes in correlation between firing of post & presynaptic neuron

Correct Answer :   synaptic strength is proportional to changes in correlation between firing of post & presynaptic neuron

Explanation : Differential hebbian learning is proportional to changes in correlation between firing of post & presynaptic neuron.

94 .
What is differential competitive learning?
A)
synaptic strength is proportional to changes of postsynaptic neuron only
B)
synaptic strength is proportional to changes of post & presynaptic neuron
C)
synaptic strength is proportional to changes of presynaptic neuron only
D)
None of the Above

Correct Answer :   None of the Above

Explanation : Differential competitive learning is based on to changes of postsynaptic neuron only.

95 .
What is reinforcement learning?
A)
learning is based on evaluative signal
B)
learning is based o desired output for an input
C)
learning is based on both desired output & evaluative signal
D)
None of the Above

Correct Answer :   learning is based on evaluative signal

Explanation : Reinforcement learning is based on evaluative signal.

96 .
What's the role of lyapunov fuction?
A)
To determine convergence
B)
To determine stability
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   To determine stability

97 .
V(x) is said to be lyapunov function if?
A)
v(x) =0
B)
v(x) >=0
C)
v(x) <=0
D)
None of the Above

Correct Answer :   v(x) <=0

Explanation : It is the condition for existence for lyapunov function.

98 .
What does cohen grossberg theorem?
A)
shows the stability of fixed weight autoassociative networks
B)
shows the stability of adaptive autoaassociative networks
C)
shows the stability of adaptive heteroassociative networks
D)
None of the Above

Correct Answer :   shows the stability of fixed weight autoassociative networks

Explanation : Cohen grossberg theorem shows the stability of fixed weight autoassociative networks.

99 .
What does cohen grossberg kosko theorem?
A)
Shows the stability of adaptive heteroassociative networks
B)
Shows the stability of adaptive autoaassociative networks
C)
Shows the stability of fixed weight autoassociative networks
D)
None of the Above

Correct Answer :   Shows the stability of adaptive autoaassociative networks

Explanation : Cohen grossberg kosko shows the stability of adaptive autoaassociative networks.

100 .
What happens during recall in neural networks?
A)
weight changes are suppressed
B)
input to the network determines the output activation
C)
both process has to happen
D)
none of the above

Correct Answer :   both process has to happen

Explanation : Follows from basic definition of Recall in a network.

101 .
Feedforward networks are used for?
A)
Pattern mapping
B)
Pattern association
C)
Pattern classification
D)
All of the Above

Correct Answer :   All of the Above

Explanation : Feedforward networks are used for pattern mapping, pattern association, pattern classification.

102 .
The network for pattern mapping is expected to perform?
A)
genaralization
B)
pattern storage
C)
pattern classification
D)
All of the Above

Correct Answer :   genaralization

Explanation : The network for pattern mapping is expected to perform genaralization.

103 .
In determination of weights by learning, for orthogonal input vectors what kind of learning should be employed?
A)
hoff learning law
B)
widrow learning law
C)
no learning law
D)
hebb learning law

Correct Answer :   hebb learning law

Explanation : For orthogonal input vectors, Hebb learning law is best suited.

104 .
In determination of weights by learning, for linear input vectors what kind of learning should be employed?
A)
hebb learning law
B)
widrow learning law
C)
hoff learning law
D)
no learning law

Correct Answer :   widrow learning law

Explanation : For linear input vectors, widrow learning law is best suited.

105 .
What are the features that can be accomplished using affine transformations?
A)
scaling
B)
translation
C)
arbitrary rotation
D)
all of the above

Correct Answer :   all of the above

Explanation : Affine transformations can be used to do arbitrary rotation, scaling, translation.

106 .
what are affine transformations?
A)
addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern
B)
addition of bias term (-1) or (+1) which results in arbitrary rotation, scaling, translation of input pattern
C)
addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern
D)
none of the above

Correct Answer :   addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern

107 .
Number of output cases depends on what factor?
A)
number of inputs
B)
total number of classes
C)
number of distinct classes
D)
None of the above

Correct Answer :   number of distinct classes

Explanation : Number of output cases depends on number of distinct classes.

108 .
What is the objective of perceptron learning?
A)
adjust weight along with class identification
B)
class identification
C)
weight adjustment
D)
none of the above

Correct Answer :   adjust weight along with class identification

109 .
In perceptron learning, what happens when input vector is correctly classified?
A)
small adjustments in weight is done
B)
no adjustments in weight is done
C)
large adjustments in weight is done
D)
weight adjustments doesn’t depend on classification of input vector

Correct Answer :   no adjustments in weight is done

Explanation : No adjustments in weight is done, since input has been correctly classified which is the objective of the system.

110 .
Two classes are said to be inseparable when?
A)
there may exist straight lines that can touch each other
B)
there is only one straight line that separates them
C)
there may exist straight lines that doesn’t touch each other
D)
all of the above

Correct Answer :   there is only one straight line that separates them

Explanation : Linearly separable classes, functions can be separated by a line.

111 .
The perceptron convergence theorem is applicable for what kind of data?
A)
binary
B)
bipolar
C)
both binary and bipolar
D)
none of the above

Correct Answer :   both binary and bipolar

Explanation : The perceptron convergence theorem is applicable for both binary and bipolar input, output data.

112 .
What is a mapping problem Neural Networks?
A)
when there are restriction but other than linear separability
B)
when there may be restrictions such as linear separability placed on input – output patterns
C)
Both (A) and (B)
D)
when no restrictions such as linear separability is placed on the set of input – output pattern pairs

Correct Answer :   when no restrictions such as linear separability is placed on the set of input – output pattern pairs

113 .
To provide generalization capability to a network, what should be done?
A)
all units should be linear
B)
all units should be non – linear
C)
except input layer, all units in other layers should be non – linear
D)
none of the above

Correct Answer :   except input layer, all units in other layers should be non – linear

Explanation : To provide generalization capability to a network, except input layer, all units in other layers should be non – linear.

114 .
The nature of mapping problem decides?
A)
number of units in third layer
B)
number of units in second layer
C)
overall number of units in hidden layers
D)
none of the above

Correct Answer :   overall number of units in hidden layers

Explanation : The nature of mapping problem decides overall number of units in hidden layers.

115 .
How is hard learning problem solved?
A)
using nonlinear differentiable output function for output layers
B)
using nonlinear differentiable output function for output and hidden layers
C)
using nonlinear differentiable output function for hidden layers
D)
it cannot be solved

Correct Answer :   using nonlinear differentiable output function for output and hidden layers

Explanation : Hard learning problem is solved by using nonlinear differentiable output function for output and hidden layers.

116 .
The number of units in hidden layers depends on?
A)
the number of inputs
B)
the number of outputs
C)
both the number of inputs and outputs
D)
the overall characteristics of the mapping problem

Correct Answer :   the overall characteristics of the mapping problem

Explanation : The number of units in hidden layers depends on the overall characteristics of the mapping problem.

117 .
Let a(l), b(l) represent in input-output pairs, where “l” varies in natural range of no.s, then if a(l)=b(l)?
A)
problem is autoassociation
B)
problem is heteroassociation
C)
can be either auto or heteroassociation
D)
none of the above

Correct Answer :   problem is autoassociation

Explanation : When a(l)=b(l) problem is classified as autoassociation.

118 .
The recalled output in pattern association problem depends on?
A)
design of network
B)
nature of input-output
C)
both input & design
D)
None of the above

Correct Answer :   both input & design

Explanation : The recalled output in pattern association problem depends on both input & design of network.

119 .
If a(l) gives output b(l) & a'=a(l)+m,where m is small quantity & if a' gives ouput b(l)+n then?
A)
network exhibits accretive behaviour
B)
network exhibits interpolative behaviour
C)
exhibits both accretive & interpolative behaviour
D)
none of the above

Correct Answer :   network exhibits interpolative behaviour

Explanation : This follows from basic definition in neural.

120 .
What is the objective of backpropagation algorithm?
A)
to develop learning algorithm for multilayer feedforward neural network
B)
to develop learning algorithm for single layer feedforward neural network
C)
to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly
D)
none of the above

Correct Answer :   to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

Explanation : The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

121 .
What is true regarding backpropagation rule?
A)
it is also called generalized delta rule
B)
there is no feedback of signal at nay stage
C)
error in output is propagated backwards only to determine weight updates
D)
all of the above

Correct Answer :   all of the above

Explanation : These all statements defines backpropagation algorithm.

122 .
What are general limitations of back propagation rule?
A)
scaling
B)
slow convergence
C)
local minima problem
D)
all of the above

Correct Answer :   all of the above

Explanation : These all are limitations of backpropagation algorithm in general.

123 .
How can learning process be stopped in backpropagation rule?
A)
on basis of average gradient value
B)
no heuristic criteria exist
C)
there is convergence involved
D)
none of the above

Correct Answer :   on basis of average gradient value

Explanation : If average gadient value fall below a preset threshold value, the process may be stopped.

124 .
What can be done by using non – linear output function for each processing unit in a feedback network?
A)
recall
B)
pattern storage
C)
pattern classification
D)
all of the above

Correct Answer :   pattern storage

Explanation : By using non – linear output function for each processing unit, a feedback network can be used for pattern storage.

125 .
The number of patterns that can be stored in a given network depends on?
A)
number of units
B)
strength of connecting links
C)
both number of units and strength of connecting links
D)
none of the above

Correct Answer :   both number of units and strength of connecting links

Explanation : The number of patterns that can be stored in a given network depends on number of units and strength of connecting links.

126 .
What happens when number of available energy minima be more than number of patterns to be stored?
A)
no effect
B)
error in recall
C)
pattern storage is not possible in that case
D)
none of the above

Correct Answer :   error in recall

Explanation : Due to additional false minima, there is error in recall.

127 .
Why there is error in recall, when number of energy minima is more the required number of patterns to be stored?
A)
due to noise
B)
due to additional false maxima
C)
due to additional false minima
D)
none of the above

Correct Answer :   due to additional false minima

128 .
How can false minima be reduced in case of error in recall in feedback neural networks?
A)
by using probabilistic update
B)
by providing additional units
C)
can be either probabilistic update or using additional units
D)
none of the above

Correct Answer :   by using probabilistic update

Explanation : Hard problem can be solved by additional units not the false minima.

129 .
What is the objective of a pattern storage task in a network?
A)
to recall a give set of patterns
B)
to store a given set of patterns
C)
both to store and recall
D)
none of the above

Correct Answer :   both to store and recall

Explanation : The objective of a pattern storage task in a network is to store and recall a given set of patterns.

130 .
What is the objective of pattern recall?
A)
it should not take place when relations are disturbed
B)
there is no such objective of recall, it depends on the system
C)
Both (A) and (B)
D)
it should take place when relations are slightly disturbed

Correct Answer :   it should take place when relations are slightly disturbed

Explanation : The pattern recall should take place even though features and their spatial relations are slightly disturbed due to noise.

131 .
How is pattern storage task generally accomplished?
A)
by a feedforward network consisting of processing units with linear output functions
B)
by a feedback network consisting of processing units with linear output functions
C)
by a feedback network consisting of processing units with non linear output functions
D)
by a feedforward network consisting of processing units with non linear output functions

Correct Answer :   by a feedback network consisting of processing units with linear output functions

Explanation : Pattern storage task generally accomplished by a feedback network consisting of processing units with non linear output functions.

132 .
The trajectory of the state is determined by?
A)
activation dynamics
B)
synaptic dynamics
C)
both activation and synaptic dynamics
D)
none of the above

Correct Answer :   activation dynamics

Explanation : The trajectory of the state is determined by activation dynamics.

133 .
what do you mean by the term trajectory of states?
A)
sates at energy minima
B)
states at energy maxima
C)
just a state of the network
D)
none of the above

Correct Answer :   none of the above

Explanation : The term trajectory of states means state of the network at successive instants of time.

134 .
What determines shape of energy landscape?
A)
network states
B)
network parameters
C)
Both (A) and (B)
D)
None of the Above

Correct Answer :   Both (A) and (B)

135 .
What is the effect of basins of attraction on energy landscape?
A)
leads to small deviations
B)
leads to fluctuation around
C)
may lead to deviation or fluctuation depends on external noise
D)
none of the above

Correct Answer :   leads to small deviations

Explanation : Basins of attraction in energy landscape leads to small deviations.

136 .
What is capacity of a network?
A)
number of inputs it can take
B)
number of output it can deliver
C)
number of patterns that can be stored
D)
none of the above

Correct Answer :   number of patterns that can be stored

Explanation : The capacity of a network is the number of patterns that can be stored.

137 .
Number of desired patterns is what of basins of attraction?
A)
dependent
B)
independent
C)
dependent or independent
D)
none of the above

Correct Answer :   independent

Explanation : Number of desired patterns is independent of basins of attraction.

138 .
What happens when number of patterns is less than number of basins of attraction?
A)
false wells
B)
storage problem becomes hard problem
C)
no storage and recall can take place
D)
none of the above

Correct Answer :   false wells

Explanation : False wells are created when number of patterns is less than number of basins of attraction.

139 .
When are false wells created?
A)
when number of patterns is same as number of basins of attraction
B)
when number of patterns is less than number of basins of attraction
C)
when number of patterns is more than number of basins of attraction
D)
none of the above

Correct Answer :   when number of patterns is less than number of basins of attraction

Explanation : False wells are created when number of patterns is less than number of basins of attraction.

140 .
When does storage problem becomes hard problem?
A)
when number of patterns is less than number of basins of attraction
B)
when number of patterns is same as number of basins of attraction
C)
Both (A) and (B)
D)
when number of patterns is more than number of basins of attraction

Correct Answer :   when number of patterns is more than number of basins of attraction

Explanation : When number of patterns is more than number of basins of attraction then storage problem becomes hard problem.

141 .
What is asynchronous update in hopfield model?
A)
all units are updated simultaneously
B)
a predefined unit is selected and its new state is computed
C)
a unit is selected at random and its new state is computed
D)
none of the above

Correct Answer :   a unit is selected at random and its new state is computed

Explanation : In asynchronous update, a unit is selected at random and its new state is computed.

142 .
If pattern is to be stored, then what does stable state should have updated value of?
A)
current sate
B)
next state
C)
both current and next state
D)
none of the above

Correct Answer :   current sate

Explanation : Stable state should have updated value of current sate.

143 .
For symmetric weights there exist?
A)
false wells
B)
fluctuations in energy landscape
C)
Both (A) and (B)
D)
basins of attraction corresponding to energy minimum

Correct Answer :   basins of attraction corresponding to energy minimum

144 .
For analysis of storage capacity what are the conditions imposed on hopfield model?
A)
symmetry of weights
B)
asynchronous update
C)
symmetry of weights and asynchronous update
D)
none of the above

Correct Answer :   symmetry of weights and asynchronous update

Explanation : For analysis of storage capacity, symmetry of weights and asynchronous update conditions are imposed on hopfield model.

145 .
p(s=1|x) = 1/(1+exp(-x/T))) ,where 's' is the output given the activation 'x' is a?
A)
sigma network
B)
stochastic network
C)
hopfield network
D)
None of the above

Correct Answer :   stochastic network

Explanation : This is the basic equation of a stochastic network.

146 .
In case of deterministic update, what kind of equilibrium is reached?
A)
Static
B)
Dynamic
C)
Neutral
D)
None of the above

Correct Answer :   Static

Explanation : In case of deterministic update, static equilibrium is reached.

147 .
In case of stochastic update, what kind of equilibrium is reached?
A)
Static
B)
Dynamic
C)
Neutral
D)
None of the above

Correct Answer :   Dynamic

Explanation : In case of stochastic update, dynamic equilibrium is reached.

148 .
When activation value is determined by using the average of fluctuations of outputs from other units, it is known as?
A)
Maximum field approximation
B)
Median field approximation
C)
Minimum field approximation
D)
None of the Above

Correct Answer :   None of the Above

Explanation : It is known as mean field approximation.

149 .
Where does a stochastic network exhibits stable states?
A)
At any temperature
B)
At critical temperature
C)
Above critical temperature
D)
Below critical temperature

Correct Answer :   Below critical temperature

Explanation : Stochastic network exhibits stable states below critical temperature.

150 .
Probability of error in recall of stored patterns can be reduced if?
A)
Patterns are stored appropriately
B)
Inputs are captured appropriately
C)
Weights are chosen appropriately
D)
None of the Above

Correct Answer :   Weights are chosen appropriately

Explanation : Probability of error in recall of stored patterns can be reduced if weights are chosen appropriately.

151 .
For what purpose is pattern environment useful?
A)
Determining structure
B)
Determining future inputs
C)
Determining desired outputs
D)
None of the Above

Correct Answer :   None of the Above

Explanation : Pattern environment is useful for determining weights.

152 .
How are energy minima related to probability of occurrence of corresponding patterns in the environment?
A)
Directly
B)
Inversely
C)
No relation
D)
Directly or inversely

Correct Answer :   Directly

Explanation : Energy minima is directly related to probability of occurrence of corresponding patterns in the environment.

153 .
What may be the reasons for non zero probability of error in recalling?
A)
Extra stable states
B)
Spurious stable states
C)
Approximation in pattern environment representation
D)
All of the Above

Correct Answer :   All of the Above

Explanation : These all are the primary reasons for existence of non zero probability of error.

154 .
How are input layer units connected to second layer in competitive learning networks?
A)
feedback manner
B)
feedforward manner
C)
feedforward or feedback
D)
feedforward and feedback

Correct Answer :   feedforward manner

Explanation : The output of input layer is given to second layer with adaptive feedforward weights.

155 .
Which layer has feedback weights in competitive neural networks?
A)
Input layer
B)
Second layer
C)
Both Input and Second layer
D)
None of the Above

Correct Answer :   Input layer

Explanation : Second layer has weights which gives feedback to the layer itself.

156 .
What is the nature of general feedback given in competitive neural networks?
A)
Self inhibitory
B)
Self excitatory
C)
Self excitatory or self inhibitory
D)
None of the Above

Correct Answer :   Self excitatory

Explanation : The output of each unit in second layer is fed back to itself in self – excitatory manner.

157 .
What consist of competitive learning neural networks?
A)
feedback paths
B)
feedforward paths
C)
either feedforward or feedback
D)
combination of feedforward and feedback

Correct Answer :   combination of feedforward and feedback

Explanation : Competitive learning neural networks is a combination of feedforward and feedback connection layers resulting in some kind of competition.

158 .
If a competitive network can perform feature mapping then what is that network can be called?
A)
Self inhibitory
B)
Self excitatory
C)
Self organization
D)
None of the Above

Correct Answer :   Self organization

Explanation : Competitive network that can perform feature mapping can be called as self organization network.

159 .
What is an instar?
A)
Receives inputs from all others
B)
Gives output to all others
C)
May receive or give input or output to others
D)
None of the above

Correct Answer :   Receives inputs from all others

Explanation : An instar receives inputs from all other input units.

160 .
How is weight vector adjusted in basic competitive learning?
A)
such that it moves towards the input vector
B)
such that it moves away from input vector
C)
such that it moves away from output vector
D)
such that it moves towards the output vector

Correct Answer :   such that it moves towards the input vector

Explanation : Weight vector is adjusted such that it moves towards the input vector.

161 .
The update in weight vector in basic competitive learning can be represented by?
A)
w(t + 1) = w(t)
B)
w(t + 1) = w(t) + del.w(t)
C)
w(t + 1) = w(t) – del.w(t)
D)
None of the above

Correct Answer :   w(t + 1) = w(t) + del.w(t)

Explanation : The update in weight vector in basic competitive learning can be represented by w(t + 1) = w(t) + del.w(t).

162 .
How is feature mapping network distinct from competitive learning network?
A)
Geometrical arrangement
B)
Significance attached to neighbouring units
C)
Nonlinear units
D)
None of the above

Correct Answer :   None of the above

Explanation : Both the geometrical arrangement and significance attached to neighbouring units make it distinct

163 .
What is true regarding adaline learning algorithm
A)
This technique allows incremental learning
B)
Error is defined as MSE between neurons net input and its desired output
C)
Uses gradient descent to determine the weight vector that leads to minimal error
D)
All of the Above

Correct Answer :   All of the Above

Explanation : Incremental learning means refining of the weights as more training samples are added, rest are basic statements that defines adaline learning.

164 .
The weight change in plain hebbian learning is?
A)
0
B)
1
C)
0 or 1
D)
None of the Above

Correct Answer :   None of the Above

Explanation : The weight change in plain hebbian learning can never be zero.

165 .
What is the nature of weights in plain hebbian learning?
A)
Divergent
B)
Convergent
C)
May be convergent or divergent
D)
None of the Above

Correct Answer :   Divergent

Explanation : In plain hebbian learning weights keep growing without bound.

166 .
What is the other name of feedback layer in competitive neural networks?
A)
Feed layer
B)
Feedback layer
C)
Competitive layer
D)
No such name exist

Correct Answer :   Competitive layer

Explanation : Feedback layer in competitive neural networks is also known as competitive layer.

167 .
What kind of feedbacks are given in competitive layer?
A)
Inhibitory to self and others
B)
Self excitatory to self and others
C)
Self excitatory to self and inhibitory to others
D)
Inhibitory to self and excitatory to others

Correct Answer :   Self excitatory to self and inhibitory to others

Explanation : The second layer of competitive networks have self excitatory to self and inhibitory to others feedbacks to make it competitive.

168 .
If the weight matrix stores the given patterns, then the network becomes?
A)
Heteroassociative memory
B)
Autoassoiative memory
C)
Temporal associative memory
D)
Multidirectional assocative memory

Correct Answer :   Autoassoiative memory

Explanation : If the weight matrix stores the given patterns, then the network becomes autoassoiative memory.

169 .
If the weight matrix stores multiple associations among several patterns, then network becomes?
A)
Autoassoiative memory
B)
Heteroassociative memory
C)
Temporal associative memory
D)
Multidirectional assocative memory

Correct Answer :   Autoassoiative memory

Explanation : If the weight matrix stores the given patterns, then the network becomes multidirectional assocative memory.

170 .
Heteroassociative memory is also known as?
A)
Unidirectional memory
B)
Bidirectional memory
C)
Temporal associative memory
D)
Multidirectional assocative memory

Correct Answer :   Bidirectional memory

Explanation : Heteroassociative memory is also known as bidirectional memory.

171 .
What is the objective of BAM?
A)
To store pattern pairs
B)
To recall pattern pairs
C)
To store a set of pattern pairs and they can be recalled by giving either of pattern as input
D)
None of the Above

Correct Answer :   To store a set of pattern pairs and they can be recalled by giving either of pattern as input

Explanation : The objective of BAM i.e Bidirectional Associative Memory, is to store a set of pattern pairs and they can be recalled by giving either of pattern as input.

172 .
Which application out of these of robots can be made of single layer feedforward network?
A)
Wall following
B)
Wall climbing
C)
Gesture control
D)
Rotating arm and legs

Correct Answer :   Wall following

Explanation : Wall following is a simple task and doesn’t require any feedback.

173 .
Which is the most direct application of neural networks?
A)
Pattern mapping
B)
Vector quantization
C)
Control applications
D)
Pattern classification

Correct Answer :   Pattern classification

Explanation : Its is the most direct and multilayer feedforward networks became popular because of this.

174 .
What are pros of neural networks over computers?
A)
They have more tolerance
B)
They have ability to learn b examples
C)
They have real time high computational rates
D)
All of the above

Correct Answer :   All of the above

Explanation : Because of their parallel structure, they have high computational rates than conventional computers, so all are true.

175 .
For what purpose, hamming network is suitable?
A)
Association
B)
Classification
C)
Pattern storage
D)
None of the above

Correct Answer :   Classification

Explanation : Hamming network performs template matching between stored templates and inputs.

176 .
What does the activation value of winner unit is indicative of?
A)
Greater the degradation less is the activation value of other units
B)
Greater the degradation more is the activation value of other units
C)
Greater the degradation less is the activation value of winning units
D)
Greater the degradation more is the activation value of winning units

Correct Answer :   Greater the degradation less is the activation value of winning units

Explanation : Simply, greater the degradation less is the activation value of winning units.

177 .
What does the matching score at first layer in recognition hamming network is indicative of?
A)
Noise immunity
B)
Dissimilarity of input pattern with patterns stored
C)
Similarity of input pattern with patterns stored
D)
None of the above

Correct Answer :   Similarity of input pattern with patterns stored

Explanation : Matching score is simply a indicative of similarity of input pattern with patterns stored.

178 .
What is the use of MLFFNN?
A)
To realize structure of MLP
B)
To solve pattern mapping problem
C)
To solve pattern classification problem
D)
To realize an approximation to a MLP

Correct Answer :   To realize an approximation to a MLP

Explanation : MLFFNN stands for multilayer feedforward network and MLP stands for multilayer perceptron.

179 .
What is the advantage of basis function over multilayer feedforward neural networks?
A)
Training of basis function is faster than MLFFNN
B)
Storing in basis function is faster than MLFFNN
C)
Training of basis function is slower than MLFFNN
D)
None of the above

Correct Answer :   Training of basis function is faster than MLFFNN

Explanation : The main advantage of basis function is that the training of basis function is faster than MLFFNN.

180 .
Why is the training of basis function is faster than MLFFNN?
A)
Because they are developed specifically for pattern classification
B)
Because they are developed specifically for pattern approximation
C)
Because they are developed specifically for pattern approximation or classification
D)
None of the above

Correct Answer :   Because they are developed specifically for pattern approximation or classification

Explanation : Training of basis function is faster than MLFFNN because they are developed specifically for pattern approximation or classification.

181 .
What does GRNN do?
A)
Pattern classification task
B)
Function approximation task
C)
Function approximation and pattern classification task
D)
None of the above

Correct Answer :   Function approximation task

Explanation : GRNN stand for Generalized Regression Neural Networks.

182 .
What does PNN do?
A)
Function approximation task
B)
Pattern classification task
C)
Function approximation and pattern classification task
D)
None of the above

Correct Answer :   Pattern classification task

Explanation : PNN stand for Probabilistic Neural Networks.

183 .
The CPN provides practical approach for implementing?
A)
Pattern clustering
B)
Patter approximation
C)
Pattern classification
D)
Pattern mapping

Correct Answer :   Pattern mapping

Explanation : CPN i.e counterpropagation network provides a practical approach for implementing pattern mapping.

184 .
How does the name counter propagation signifies its architecture?
A)
Its ability to learn inverse mapping functions
B)
Its ability to learn forward mapping functions
C)
Its ability to learn forward and inverse mapping functions
D)
None of the above

Correct Answer :   Its ability to learn forward and inverse mapping functions

Explanation : Counter propagation network has ability to learn forward and inverse mapping functions.

185 .
An auto – associative network is?
A)
Network in neural which contains feedback
B)
Network in neural which no loops
C)
Network in neural which contains loops
D)
None of the above

Correct Answer :   Network in neural which contains feedback

Explanation : An auto – associative network contains feedback.

186 .
What is true about sigmoidal neurons?
A)
Outputs a real number between 0 and 1
B)
They are the most common type of neurons
C)
Can accept any vectors of real numbers as input
D)
All of the above

Correct Answer :   All of the above

Explanation : These all statements itself defines sigmoidal neurons.

187 .
What does ART stand for?
A)
Automatic Resonance Theory
B)
Adaptive Resonance Theory
C)
Artificial Resonance Theory
D)
None of the Above

Correct Answer :   Adaptive Resonance Theory

Explanation : ART stand for Adaptive Resonance Theory.

188 .
What does vigilance parameter in ART determines?
A)
Number of desired outputs
B)
Number of possible outputs
C)
Number of acceptable inputs
D)
None of the above

Correct Answer :   None of the above

Explanation : Vigilance parameter in ART determines the tolerance of matching process.

189 .
What type of inputs does ART – 1 receives?
A)
binary
B)
bipolar
C)
both bipolar and binary
D)
none of the above

Correct Answer :   binary

Explanation : Adaptive resonance theory take care of stability plasticity dilemma.

190 .
A greater value of 'p' the vigilance parameter leads to?
A)
No change
B)
Bigger clusters
C)
Small clusters
D)
None of the above

Correct Answer :   Small clusters

Explanation : Input samples associated with same neuron get reduced.

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