Self organizing neural network method and system for general classification of patterns

摘要

A neural network system and method that can adaptively recognize each of many pattern configurations from a set. The system learns and maintains accurate associations between signal pattern configurations and pattern classes with training from a teaching mechanism. The classifying system consists of a distributed input processor and an adaptive association processor. The input processor decomposes an input pattern into modules of localized contextual elements. These elements in turn are mapped onto pattern classes using a self- organizing associative neural scheme. The associative mapping determines which pattern class best represents the input pattern. The computation is done through gating elements that correspond to the contextual elements. Learning is achieved by modifying the gating elements from a true/false response to the computed probabilities for all classes in the set. The system is a parallel and fault tolerant process. It can easily be extended to accommodate an arbitrary number of patterns at an arbitrary degree of precision. The classifier can be applied to automated recognition and inspection of many different types of signals and patterns.