RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.

Original languageEnglish
Pages (from-to)2636-2642
Number of pages7
JournalNeurocomputing
Volume72
Issue number10-12
DOIs
Publication statusPublished - 2009 Jun 1

Fingerprint

Fuzzy neural networks
Membership functions
Splines
Nonlinear systems
Tuning
Genetic algorithms
Controllers
Closed loop systems
Genes

Keywords

  • Adaptive fuzzy-neural control
  • B-spline membership function
  • Fuzzy-neural network
  • Reduced-form genetic algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

Cite this

RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems. / Leu, Yih-Guang; Wang, Wei-Yen; Li, I. Hsum.

In: Neurocomputing, Vol. 72, No. 10-12, 01.06.2009, p. 2636-2642.

Research output: Contribution to journalArticle

@article{766a7b9307e546d39eb186e46531b700,
title = "RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems",
abstract = "In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.",
keywords = "Adaptive fuzzy-neural control, B-spline membership function, Fuzzy-neural network, Reduced-form genetic algorithm",
author = "Yih-Guang Leu and Wei-Yen Wang and Li, {I. Hsum}",
year = "2009",
month = "6",
day = "1",
doi = "10.1016/j.neucom.2008.10.005",
language = "English",
volume = "72",
pages = "2636--2642",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",
number = "10-12",

}

TY - JOUR

T1 - RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems

AU - Leu, Yih-Guang

AU - Wang, Wei-Yen

AU - Li, I. Hsum

PY - 2009/6/1

Y1 - 2009/6/1

N2 - In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.

AB - In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.

KW - Adaptive fuzzy-neural control

KW - B-spline membership function

KW - Fuzzy-neural network

KW - Reduced-form genetic algorithm

UR - http://www.scopus.com/inward/record.url?scp=67349219690&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67349219690&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2008.10.005

DO - 10.1016/j.neucom.2008.10.005

M3 - Article

AN - SCOPUS:67349219690

VL - 72

SP - 2636

EP - 2642

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

IS - 10-12

ER -