Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control

被引：83

作者：

Lin, Chih-Min ^{[1
]}

Tien-Loc Le ^{[1
,2
]}

Tuan-Tu Huynh ^{[1
,2
]}

机构：

[1] Yuan Ze Univ, Taoyuan, Taiwan

[2] Lac Hong Univ, Dept Elect Elect & Mech Engn, Bien Hoa, Vietnam

来源：

NEUROCOMPUTING | 2018年 / 275卷

关键词：

System identification; Interval type-2 fuzzy system; Neural network; Self-evolving algorithm; LEARNING ALGORITHM; DESIGN; OPTIMIZATION;

D O I：

10.1016/j.neucom.2017.11.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Determining a network size for a fuzzy neural network structure is very important, and it is often difficult to obtain the most suitable value. This study develops a self-evolving function-link interval type-2 fuzzy neural network (SEFT2FNN) that autonomously constructs the rule base with the initial empty and the membership functions. The function-link is applied to an interval type-2 fuzzy neural network to give a more accurate approximation of the function. The adaptive laws for the proposed system are derived using the steepest descent gradient approach. The stability of system was guaranteed using Lyapunov function approach. Finally, the performance of the proposed system is verified using the numerical simulations of the nonlinear system identification and the control of time-varying plants. (c) 2017 Elsevier B.V. All rights reserved.

引用

页码：2239 / 2250

页数：12

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