Dynamic adaptive learning algorithm based on two-fuzzy neural-networks

被引：8
|
作者：
Meng, Dan [1 ]
Pei, Zheng [2 ]
机构：
[1] Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Chengdu 611130, Peoples R China
[2] Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China
来源：
NEUROCOMPUTING | 2014年 / 125卷
基金：
中国国家自然科学基金;
关键词：
Dynamic adaptive learning algorithm; Fuzzy rules; Fuzzy neural network; Partially unknown nonlinear control system; FUZZY CONTROL; CONTROLLER; SYSTEMS; IDENTIFICATION;
D O I：
10.1016/j.neucom.2012.07.048
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A dynamic adaptive learning algorithm based on two fuzzy neural-networks for the control of a partially unknown nonlinear dynamic system is developed in this paper. The proposed fuzzy neural-network controller is composed of a computation controller and a learning controller. The computation controller and a learning controller will control collaboratively for partially unknown nonlinear dynamic system. Formally, the stability of the control system and convergence of the fuzzy neural-network have been proved. The proposed algorithm based on two fuzzy neural-networks can avoid the time-consuming trial-and-error tuning procedure for determining structure and parameters. The simulation experiment shows that the proposed method is feasible, valid and rational. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：88 / 94
页数：7
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