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

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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
Issue number10-12
Publication statusPublished - 2009 Jun 1



  • 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

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