MIMO Evolving Learning based on Maximum Likelihood Algorithm Applied to Black Box Fuzzy Modeling for Systems Identification Design

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：
ICIMCO 2015 PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL. 1 | 2015年
关键词：
Evolving Fuzzy Systems; Instrumental Variable; Recursive Estimation; Takagi-Sugeno Fuzzy Model; Maximum Likelihood;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an overview of a specific application to computational intelligence techniques, specifically, evolving fuzzy systems: online fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion online with instrumental variable recursive parameter estimation. The performance and application of the proposed methodology is based on the black box modeling.
引用
收藏
页码：199 / 206
页数：8
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