Nonlinear systems modeling via fuzzy logic rules

Hongwei Wang; Guangfu Ma

Nonlinear systems modeling via fuzzy logic rules

Hongwei Wang^*
, Guangfu Ma

^*Corresponding author for this work

School of Astronautics

National University of Defense Technology

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

4 Scopus citations

Abstract

We propose a new self-tuning fuzzy modeling by means of fuzzy clustering. Based on fuzzy clustering, the adaptive fuzzy inference is used to modify the fuzzy system. Moreover, based on this modified fuzzy system, the paper presents an on-line identifying algorithm with which the on-line parameter estimation of nonlinear system is realized. To demonstrate the applicability of the proposed method, simulation results relative to a few examples are presented in the end.

Original language	English
Journal	Kongzhi Lilun Yu Yingyong/Control Theory and Applications
Volume	17
Issue number	3
State	Published - 2000

Keywords

Kalman filtering algorithm
Nonlinear system modeling
On-line identification
Recursive fuzzy clustering
System identificationj fuzzy systems

Cite this

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title = "Nonlinear systems modeling via fuzzy logic rules",

abstract = "We propose a new self-tuning fuzzy modeling by means of fuzzy clustering. Based on fuzzy clustering, the adaptive fuzzy inference is used to modify the fuzzy system. Moreover, based on this modified fuzzy system, the paper presents an on-line identifying algorithm with which the on-line parameter estimation of nonlinear system is realized. To demonstrate the applicability of the proposed method, simulation results relative to a few examples are presented in the end.",

keywords = "Kalman filtering algorithm, Nonlinear system modeling, On-line identification, Recursive fuzzy clustering, System identificationj fuzzy systems",

author = "Hongwei Wang and Guangfu Ma",

year = "2000",

language = "英语",

volume = "17",

journal = "Kongzhi Lilun Yu Yingyong/Control Theory and Applications",

issn = "1000-8152",

publisher = "South China University of Technology",

number = "3",

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TY - JOUR

T1 - Nonlinear systems modeling via fuzzy logic rules

AU - Wang, Hongwei

AU - Ma, Guangfu

PY - 2000

Y1 - 2000

N2 - We propose a new self-tuning fuzzy modeling by means of fuzzy clustering. Based on fuzzy clustering, the adaptive fuzzy inference is used to modify the fuzzy system. Moreover, based on this modified fuzzy system, the paper presents an on-line identifying algorithm with which the on-line parameter estimation of nonlinear system is realized. To demonstrate the applicability of the proposed method, simulation results relative to a few examples are presented in the end.

AB - We propose a new self-tuning fuzzy modeling by means of fuzzy clustering. Based on fuzzy clustering, the adaptive fuzzy inference is used to modify the fuzzy system. Moreover, based on this modified fuzzy system, the paper presents an on-line identifying algorithm with which the on-line parameter estimation of nonlinear system is realized. To demonstrate the applicability of the proposed method, simulation results relative to a few examples are presented in the end.

KW - Kalman filtering algorithm

KW - Nonlinear system modeling

KW - On-line identification

KW - Recursive fuzzy clustering

KW - System identificationj fuzzy systems

UR - https://www.scopus.com/pages/publications/0141524467

M3 - 文章

AN - SCOPUS:0141524467

SN - 1000-8152

VL - 17

JO - Kongzhi Lilun Yu Yingyong/Control Theory and Applications

JF - Kongzhi Lilun Yu Yingyong/Control Theory and Applications

IS - 3

ER -

Nonlinear systems modeling via fuzzy logic rules

Abstract

Keywords

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