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

研究成果: 雜誌貢獻文章

17 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)2636-2642
頁數7
期刊Neurocomputing
72
發行號10-12
DOIs
出版狀態已發佈 - 2009 六月 1

    指紋

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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