PNS modules for the synthesis of parallel self-organizing hierarchical neural networks

The PNS module is discussed as the building block for the synthesis of parallel, self-organizing, hierarchical, neural networks (PSHNN). The P- and NS-units are fractile in nature, meaning that each such unit may itself consist of a number of parallel PNS modules. Through a mechanism of statistical acceptance or rejection of input vectors for classification, the sample space is divided into a number of subspaces. The input vectors belonging to each subspace are classified by a dedicated set of PNS modules. This strategy results in considerably higher accuracy of classification and better generalization as compared to previous neural network models.<
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