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

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.4 pp.1440-1449

Publication Date: 2006/04/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.4.1440

Type of Manuscript: PAPER

Category: Computation and Computational Models

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Chunshien LI, Kuo-Hsiang CHENG, Zen-Shan CHANG, Jiann-Der LEE, "Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 4, pp. 1440-1449, April 2006, doi: 10.1093/ietisy/e89-d.4.1440.
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.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.4.1440/_p

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@ARTICLE{e89-d_4_1440,
author={Chunshien LI, Kuo-Hsiang CHENG, Zen-Shan CHANG, Jiann-Der LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification},
year={2006},
volume={E89-D},
number={4},
pages={1440-1449},
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.},
keywords={},
doi={10.1093/ietisy/e89-d.4.1440},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification
T2 - IEICE TRANSACTIONS on Information
SP - 1440
EP - 1449
AU - Chunshien LI
AU - Kuo-Hsiang CHENG
AU - Zen-Shan CHANG
AU - Jiann-Der LEE
PY - 2006
DO - 10.1093/ietisy/e89-d.4.1440
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2006
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.
ER -