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http://dx.doi.org/10.5394/KINPR.2004.28.3.221

A Study on Optimal Neural Network Structure of Nonlinear System using Genetic Algorithm  

Kim, Hong-Bok (Dept. of Control and Instrumentation Engineering, Korea Maritime University)
Kim, Jeong-Keun (Dept. of Control and Instrumentation Engineering, Korea Maritime University)
Kim, Min-Jung (Research Institute of Industry Technology, Korea Maritime University)
Hwang, Seung-Wook (Dept of Mechanical and Information Engineering, Korea Maritime University)
Publication Information
Journal of Navigation and Port Research / v.28, no.3, 2004 , pp. 221-225 More about this Journal
Abstract
This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.
Keywords
Nonlinear system; Neural network; Genetic algorithm; System identification;
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