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GA-Based Generation of Fuzzy Rules

In: Fuzzy Evolutionary Computation

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  • Oliver Nelles

    (Darmstadt University of Technology, Institute of Automatic Control, Laboratory of Control Engineering and Process Automation)

Abstract

This chapter deals with fuzzy rule generation completed with the aid of genetic algorithms (GAs). Often a relationship cannot be fully analyzed theoretically from first principles but measured data and qualitative knowledge in the form of rules are available. Then fuzzy rule-based systems offer the advantage of describing nonlinear mappings in a more interpretable way than other approaches. On one hand, they allow to initialize the system with expert knowledge in order to complete a successive data-based tuning step faster. On the other hand, a trained fuzzy system can be interpreted by the user. The training and interpretation steps can be iterated until the obtained system exhibits not only a satisfactory performance but delivers reasonable interpretation abilities. Compared to black-box approaches, this gives the user a much higher confidence in the system and significantly increases the acceptance in industrial applications.

Suggested Citation

  • Oliver Nelles, 1997. "GA-Based Generation of Fuzzy Rules," Springer Books, in: Witold Pedrycz (ed.), Fuzzy Evolutionary Computation, chapter 3, pages 269-295, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6135-4_12
    DOI: 10.1007/978-1-4615-6135-4_12
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