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Identifying fuzzy models utilizing genetic programming
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DOI：10.1016/S0165-0114(98)00086-4.png)
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Abstract
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Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasized that the identification of a fuzzy model is a complex optimization task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
system identification
fuzzy modeling
genetic programming
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