Modeling of nonlinear static system via neural network based intelligent technology

Authors:
Dong-Won Kim;Jang-Hyun Park;Sam-Jun Seo;Gwi-Tae Park
Affiliations:
Department of Electrical Engineering, Korea University, Seoul, Korea;Department of Control System Engineering, Mokpo National University, Muan-gun, Chonnam, Korea;Department of Electrical & Electronic Engineering, Anyang University, Anyang-shi, Kyunggi-do, Korea;Department of Electrical Engineering, Korea University, Seoul, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

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Abstract

Modeling of nonlinear static system using neural network based intelligent technology is presented in this paper. The architecture of the intelligent system is combined neural network with polynomial neural network. The composite architecture is designed to get a heuristic approximation method for nonlinear static system modeling. Owing to the approximation capabilities, neural networks have been widely utilized to process modeling, whereas the polynomial neural network is an analysis technique for identifying nonlinear relationships between inputs and outputs of the target system. So the hybrid architecture can harmonize the advantages of the each modeling methodology. Simulation results of the intelligent technology will be shown efficient and good performance.