GA-Based Generation of Fuzzy Rules

Oliver Nelles
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Oliver Nelles: Darmstadt University of Technology, Institute of Automatic Control, Laboratory of Control Engineering and Process Automation

Chapter 2.9 in Fuzzy Evolutionary Computation, 1997, pp 269-295 from Springer

Abstract: 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.

Keywords: Membership Function; Fuzzy System; Fuzzy Rule; Rule Structure; Output Membership Function (search for similar items in EconPapers)
Date: 1997
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DOI: 10.1007/978-1-4615-6135-4_12

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