HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS

被引：0

作者：

Lavygina, A. ^{[1
]}

Hodashinsky, I. ^{[1
]}

机构：

[1] Tomsk State Univ Control Syst & Radioelect, Dept Data Proc Automat, 40 Lenina St, Tomsk, Russia

来源：

ECTA 2011/FCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION THEORY AND APPLICATIONS AND INTERNATIONAL CONFERENCE ON FUZZY COMPUTATION THEORY AND APPLICATIONS | 2011年

基金：

俄罗斯基础研究基金会;

关键词：

Fuzzy Modeling; Parameter Estimation; Hybrid Algorithm; Genetic Algorithm; Gradient Descent Method; Kalman Filter; Least Squares Method;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hybrid method for estimation of fuzzy model parameters is presented. The main idea of the method is to apply gradient descent method or Kalman filter as a mutation operator of genetic algorithm for estimation of antecedent parameters of fuzzy "IF-THEN" rules. Thus, part of the individuals in the population mutate by means of gradient descent method or Kalman filter, the others mutate in an ordinary way. Once antecedents are tuned, consequents tuning is performed with the least squares method. The results of computer experiment are presented.

引用

页码：513 / 515

页数：3

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