Weighted Fuzzy Genetic Programming Algorithm for Structure and Parameters Selection of Fuzzy Systems for Nonlinear Modelling

被引：0
|
作者：
Lapa, Krystian [1 ]
Cpalka, Krzysztof [1 ]
机构：
[1] Czestochowa Tech Univ, Inst Computat Intelligence, Czestochowa, Poland
来源：
INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY - ISAT 2016 - PT I | 2017年 / 521卷
关键词：
Genetic programming; Weights; Fuzzy system; Nonlinear modelling; Dynamic systems;
D O I：
10.1007/978-3-319-46583-8_13
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a weighted fuzzy genetic programming algorithm for selection of structure and parameters of fuzzy systems for nonlinear modelling is proposed. This method is based on fuzzy genetic programming and innovations in this method concern, among the others, using weights of fuzzy aggregation operators, using weights of fuzzy rules, using fitness function criteria designed for fuzzy genetic programming and using dynamic links between fuzzy rules and fuzzy rules base. The proposed method was tested with use of typical nonlinear modelling problems.
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页码：157 / 174
页数：18
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