Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification(Computation and Computational Models)
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概要
- 論文の詳細を見る
A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.
- 社団法人電子情報通信学会の論文
- 2006-04-01
著者
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Lee Jiann‐der
Chang Gung Univ. Tao‐yuan Twn
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Lee Jiann-der
Department Of Electrical Engineering Chang Gung University
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Cheng Kuo-hsiang
Department Of Electrical Engineering Chang Gung University
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LI Chunshien
Department of Computer Science and Information Engineering, National University of Tainan
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CHANG Zen-Shan
Department of Electrical Engineering, Chang Gung University
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Chang Zen-shan
Department Of Electrical Engineering Chang Gung University
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Li Chunshien
Department Of Computer Science And Information Engineering National University Of Tainan
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