Hybrid architecture of the neural networks and self-organizing approximator technique: A new approach to nonlinear system modeling

Abstract

We discuss a new hybrid architecture that dwells on the ideas of combining neural networks (NNs) with self-organizing approximators (self-organizing polynomial neural networks: SOPNN). The hybrid system is combined to get a novel heuristic approximation method which can find a workable synergistic environment for nonlinear system modeling. NNs have been used successfully in order to describe the static and dynamics of nonlinear process system. SOPNN is an analysis technique for identifying nonlinear relationships between inputs and outputs of the system and builds hierarchical polynomial regressions of required complexity. To harmonize NN with SOPNN and find a workable synergistic environment, systematic approach, neural networks based SOPNN, has been investigated and contained its comparative study in this paper. Identification results of nonlinear system with two inputs will be demonstrated to show the performance of the proposed approach. As a result, it is shown that the proposed method is efficient and much more accurate than either of the two individual schemes as well as other modeling methods.