FEATURES OF ARTIFICIAL NETWORK (ANN)
Artificial neural networks may by physical devices or simulated on conventional computers. From a practical point of view, an ANN is just a parallel computational system consisting of many simple processing elements connected together in a specific way in order to perform a particular task. There are some important features of artificial networks as follows.
(1) Artificial neural networks are extremely powerful computational devices (Universal computers).
(2) ANNs are modeled on the basis of current brain theories, in which information is represented by weights.
(3) ANNs have massive parallelism which makes them very efficient.
(4) They can learn and generalize from training data so there is no need for enormous feats of programming.
(5) Storage is fault tolerant i.e. some portions of the neural net can be removed and there will be only a small degradation in the quality of stored data.
(6) They are particularly fault tolerant which is equivalent to the “graceful degradation” found in biological systems.
(7) Data are naturally stored in the form of associative memory which contrasts with conventional memory, in which data are recalled by specifying address of that data.
(8) They are very noise tolerant, so they can cope with situations where normal symbolic systems would have difficulty.
(9) In practice, they can do anything a symbolic/ logic system can do and more.
(10) Neural networks can extrapolate and intrapolate from their stored information. The neural networks can also be trained. Special training teaches the net to look for significant features or relationships of data.
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