A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation

Wooyong CHUNG; Euntai KIM

doi:10.1093/ietisy/e89-d.9.2473

A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation

Wooyong CHUNG, Euntai KIM

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Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.9 pp.2473-2483

Publication Date: 2006/09/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.9.2473

Type of Manuscript: PAPER

Category: Computation and Computational Models

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Wooyong CHUNG, Euntai KIM, "A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 9, pp. 2473-2483, September 2006, doi: 10.1093/ietisy/e89-d.9.2473.
Abstract: Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.9.2473/_p

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@ARTICLE{e89-d_9_2473,
author={Wooyong CHUNG, Euntai KIM, },
journal={IEICE TRANSACTIONS on Information},
title={A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation},
year={2006},
volume={E89-D},
number={9},
pages={2473-2483},
abstract={Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.},
keywords={},
doi={10.1093/ietisy/e89-d.9.2473},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation
T2 - IEICE TRANSACTIONS on Information
SP - 2473
EP - 2483
AU - Wooyong CHUNG
AU - Euntai KIM
PY - 2006
DO - 10.1093/ietisy/e89-d.9.2473
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2006
AB - Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.
ER -