Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

The human brain processes information flows continuously from the external environment. However, it can modify and update the stored images, and create new, without destroying what previously memorized. Thus it differs significantly from the majority of neural networks as neural networks (NN), trained by back propagation, genetic algorithms, in bidirectional associative memory, Hopfield networks, etc. very often a new way of learning, situation or association significantly distorts or even destroys the fruits of prior learning, requiring a change in a significant part of weights of connections or complete ret raining of the network [1-4]. Impossibility of using the specified NN solve the problem of stability-plasticity, that is a problem of perception and memorization of new information without loss or distortion of existing, was one of the main reasons for the development of fundamentally new configurations of neural networks. Examples of such networks are neural networks, derived from the adaptive resonance theory (ART), developed by Carpenter and Grossberg [5, 6].