An adaptive second order fuzzy neural network for nonlinear system modeling

被引：37
|
作者：
Han, Hong-Gui [1 ,2 ]
Ge, Lu-Ming [1 ,2 ]
Qiao, Jun-Fei [1 ,2 ]
机构：
[1] Beijing Univ Technol, Coll Elect & Control Engn, Beijing 100124, Peoples R China
[2] Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 214卷
基金：
中国博士后科学基金; 美国国家科学基金会;
关键词：
Nonlinear system modeling; Fuzzy neural network; Adaptive second-order algorithm; Fast convergence; DYNAMICAL-SYSTEMS; ALGORITHM; IDENTIFICATION; CLASSIFICATION; OPTIMIZATION; PREDICTION; SLUDGE;
D O I：
10.1016/j.neucom.2016.07.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an adaptive second order algorithm (ASOA) has been developed to train the fuzzy neural network (FNN) to achieve fast and robust convergence for nonlinear system modeling. Different from recent studies, this ASOA-based FNN (ASOA-FNN) has the quasi Hessian matrix and gradient vector which are accumulated as the sum of related sub matrices and vectors, respectively. Meanwhile, the learning rate of ASOA-FNN is designed to accelerate the learning speed. In addition, the convergence of the proposed ASOA-FNN has been proved both in the fixed learning rate phase and the adaptive learning rate phase. Finally, several comparisons have been realized and they have shown that the proposed ASOA-FNN has faster convergence speed and more accurate results than that of some existing methods. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：837 / 847
页数：11
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