Identifying fuzzy models utilizing genetic programming

被引：53

作者：

Bastian, A ^{[1
]}

机构：

[1] Volkswagen AG, Elect Res, D-38436 Wolfsburg, Germany

来源：

FUZZY SETS AND SYSTEMS | 2000年 / 113卷 / 03期

关键词：

system identification; fuzzy modeling; genetic programming;

D O I：

10.1016/S0165-0114(98)00086-4

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：333 / 350

页数：18

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