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Book Chapter

Learning the Rule Base of a Fuzzy Controller by a Genetic Algorithm


Hopf,  Jörn
Programming Logics, MPI for Informatics, Max Planck Society;

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Hopf, J., & Klawonn, F. (1994). Learning the Rule Base of a Fuzzy Controller by a Genetic Algorithm. In R. Kruse, R. Palm, & J. Gebhardt (Eds.), Fuzzy Systems in Computer Science (pp. 63-74). Braunschweig, Germany: Friedr. Vieweg & Sohn.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-AD8C-1
For the design of a fuzzy controller it is necessary to choose, besides other parameters, suitable membership functions for the linguistic terms and to determine a rule base.\\ This paper deals with the problem of finding %out the best possible rule a good rule base --- the basis of a fuzzy controller. Consulting experts still is the usual but time--consuming and therefore rather expensive method. Besides, after having designed the controller, one cannot be sure that the rule base %works at its approximative optimum. will lead to near optimal control. This paper shows how to reduce significantly the period of development (and the costs) of fuzzy controllers with the help of genetic algorithms and, above all, how to engender a rule base which is very close to an optimum solution.\\ The example of the inverted pendulum is used to demonstrate how a genetic algori thm can be designed for an automatic construction of a rule base.\\ So this paper does {\em not} deal with the tuning of an existing fuzzy controller but with the genetic (re--)production of rules, even without the need for experts. Thus, a program is engendered, consisting of simple ``{\sl IF \dots THEN \dots}'' instructions.