On Line Fuzzy Learning Maximum Likelihood-Instrumental Variable Evolving Algorithm

被引：0

作者：

Rocha Filho, Orlando Donato ^{[1
]}

de Oliveira Serra, Ginalber Luiz ^{[1
]}

机构：

[1] Fed Inst Educ Sci & Technol, Dept Electroelect, Lab Computat Intelligence Appl Techonol, Sao Luis, MA, Brazil

来源：

2015 LATIN AMERICA CONGRESS ON COMPUTATIONAL INTELLIGENCE (LA-CCI) | 2015年

关键词：

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：

In this paper an evolving recursive fuzzy cluster algorithm based on maximum likelihood criterion using the recursive instrumental variable parameter estimation for nonlinear system identification, is proposed. The performance of the proposed methodology is illustrated for black box modeling of a thermal plant from real-time acquisition data plataform. The experimental results are evaluated from metrics used in the literature to show the efficiency of the proposed online evolving recursive fuzzy clustering algorithm.

引用

页数：6

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