Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification

Authors:
Dongwon Kim;Sam-Jun Seo;Gwi-Tae Park
Affiliations:
Department of Electrical Engineering and Computer Sciences, University of California Berkeley, CA 94720, United States and Department of Electrical Engineering, Korea University, Republic of Korea;Department of Electrical and Electronic Engineering, Anyang University, Republic of Korea;Department of Electrical Engineering, Korea University, Republic of Korea
Venue:
Advances in Engineering Software
Year:
2009

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Abstract

This paper presents a novel hybrid GMDH-type algorithm which combines neural networks (NNs) with an approximation scheme (self-organizing polynomial neural network: SOPNN). This composite structure is developed to establish a new heuristic approximation method for identification of nonlinear static systems. NNs have been widely employed to process modeling and control because of their approximation capabilities. And SOPNN is an analysis technique for identifying nonlinear relationships between the inputs and outputs of such systems and builds hierarchical polynomial regressions of required complexity. Therefore, the combined model can harmonize NNs with SOPNN and find a workable synergistic environment. Simulation results of the nonlinear static system are provided to show that the proposed method is much more accurate than other modeling methods. Thus, it can be considered for efficient system identification methodology.