Structure Selection and Identification of Hammerstein Type Nonlinear Systems Using Automatic Choosing Function Model and Genetic Algorithm(<Special Section>Nonlinear Theory and its Applications)
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概要
- 論文の詳細を見る
This paper presents a novel method of structure selection and identification for Hammerstein type nonlinear systems. An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The connection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. The adjusting parameters for the ACF model structure, i.e. the number and widths of the subdomains and the shape of the ACF are properly selected by using a genetic algorithm, in which the Akaike information criterion is utilized as the fitness value function. The effectiveness of the proposed method is confirmed through numerical experiments.
- 社団法人電子情報通信学会の論文
- 2005-10-01
著者
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Takata Hitoshi
Kagoshima Univ. Kagoshima Jpn
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Takata Hitoshi
Department Of Electrical And Electronics Engineering Kagoshima University
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Hachino Tomohiro
Department Of Electrical And Electronics Engineering Kagoshima University
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Takata Hitoshi
Department Of Computer Science Kyushu Institute Of Technology
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