Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification

  • Authors:

  • Chunshien Li;Kuo-Hsiang Cheng;Zen-Shan Chang;Jiann-Der Lee

  • Affiliations:

  • The author is with the Department of Computer Science and Information Engineering, National University of Tainan, Tainan, 700, Taiwan, R. O. C.,;The authors are with the Department of Electrical Engineering, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan, 333, Taiwan, R.O.C. E-mail: D9021010@stmail.cgu.edu.tw;The authors are with the Department of Electrical Engineering, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan, 333, Taiwan, R.O.C. E-mail: D9021010@stmail.cgu.edu.tw;The authors are with the Department of Electrical Engineering, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan, 333, Taiwan, R.O.C. E-mail: D9021010@stmail.cgu.edu.tw

  • Venue:
  • IEICE - Transactions on Information and Systems
  • Year:
  • 2006

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Abstract

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.