Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks Illustrated with the Case of Non-linear Identification

被引：0

作者：

Castro, Juan R. ^{[1
]}

Castillo, Oscar ^{[2
]}

Melin, Patricia ^{[2
]}

Rodriguez-Diaz, Antonio ^{[1
]}

Mendoza, Olivia ^{[1
]}

机构：

[1] Baja California Autonomous Univ, Tijuana, BC, Mexico

[2] Tijuana Inst Technol, Div Grad Studies & Res, Tijuana, BC, Mexico

来源：

PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | 2009年

关键词：

Interval Type-2 Fuzzy Logic Systems; Interval Type-2 Fuzzy Neural Networks; Neural Networks; Universal Approximation; SYSTEMS;

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：

Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we show that an Interval Type-2 Fuzzy Neural Network (IT2FNN) is a universal approximator with some precision using a set of rules and Interval Type-2 membership functions (IT2MF) and the Stone-Weierstrass Theorem. Also, simulation results of non-linear function identification using the IT2FNN for one and three variables with 10-fold cross-validation are presented.

引用

页码：1382 / 1387

页数：6

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