Parallel processing capability : Artificial neural networks have a numerical value that can perform more than one task simultaneously.
Storing data on the entire network : Data that is used in traditional programming is stored on the whole network, not on a database. The disappearance of a couple of pieces of data in one place doesn't prevent the network from working.
Capability to work with incomplete knowledge : After ANN training, the information may produce output even with inadequate data. The loss of performance here relies upon the significance of missing data.
Having a memory distribution : For ANN is to be able to adapt, it is important to determine the examples and to encourage the network according to the desired output by demonstrating these examples to the network. The succession of the network is directly proportional to the chosen instances, and if the event can't appear to the network in all its aspects, it can produce false output.
Having fault tolerance : Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature makes the network fault-tolerance.