Journal of Mechanical Science and Technology

The Korean Society of Mechanical Engineers (대한기계학회)
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Development of Global Function Approximations of Desgin optimization Using Evolutionary Fuzzy Modeling

  • Kim, Seungjin (Department of Mechanical Engineering, Yonsei University) ;
  • Lee, Jongsoo (Department of Mechanical Engineering, Yonsei University)
  • Published : 2000.11.01
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Abstract

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

Keywords

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