Evolving Fuzzy Clustering Algorithm based on Maximum Likelihood with Participatory Learning

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：

PROCEEDINGS OF THE 2016 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS) | 2016年

关键词：

IDENTIFICATION;

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：

This paper presents a fuzzy clustering algorithm based on maximum likelihood with participatory learning. The adopted methodology is based on an online fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The performance and application of the proposed algorithm is based on the black box modeling of nonlinear system widely cited in the literature.

引用

页码：65 / 72

页数：8

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