Hybrid evolutionary soft-computing approach for unknown system identification

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

doi:10.1093/ietisy/e89-d.4.1440

Hybrid evolutionary soft-computing approach for unknown system identification

Chunshien Li, Kuo Hsiang Cheng, Zen Shan Chang, Jiann Der Lee

Department of Information Management

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	1440-1449
Number of pages	10
Journal	IEICE Transactions on Information and Systems
Volume	E89-D
Issue number	4
DOIs	https://doi.org/10.1093/ietisy/e89-d.4.1440
State	Published - Apr 2006

Keywords

Hybrid learning
Neuro-fuzzy systems
Random optimization
System identification / modeling
Weighted Gaussian function

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10.1093/ietisy/e89-d.4.1440

Cite this

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title = "Hybrid evolutionary soft-computing approach for unknown system identification",

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.",

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AU - Cheng, Kuo Hsiang

AU - Chang, Zen Shan

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AB - 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.

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Hybrid evolutionary soft-computing approach for unknown system identification

Abstract

Keywords

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