Parallel structures and self-organization of heterogeneous polynomial neural networks for pattern recognition and diagnostics of states

Abstract

We consider different parallel architectures and methods for self-organization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of pattern recognition and in diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows.