Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks

  • Chin-Teng Lin
  • Cheng-Hung Chen
  • Cheng-Jian Lin


This study presents a functional-link-based neuro-fuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.


Root Mean Square Error Membership Function Fuzzy System Fuzzy Rule Fuzzy Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer London 2009

Authors and Affiliations

  • Chin-Teng Lin
    • 1
  • Cheng-Hung Chen
    • 1
  • Cheng-Jian Lin
    • 2
  1. 1.Department of Electrical and Control EngineeringNational Chiao-Tung UniversityHsinchuChina
  2. 2.Department of Computer Science and Information EngineeringNational Chin-Yi University of TechnologyTaiping City, Taichung CountyChina

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