Nonlinear systems control using self-constructing wavelet networks

被引：25

作者：

Lin, Cheng-Jian ^{[1
,2
]}

机构：

[1] Natl Univ Kaohsiung, Dept Elect Engn, Kaohsiung 811, Taiwan

[2] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 411, Taichung County, Taiwan

来源：

APPLIED SOFT COMPUTING | 2009年 / 9卷 / 01期

关键词：

Temperature control; Wavelet neural networks; Online learning; Back-propagation; Degree measure; FUZZY NEURAL-NETWORK;

D O I：

10.1016/j.asoc.2008.03.014

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 describes a self-constructing wavelet network (SCWN) controller for nonlinear systems control. The proposed SCWN controller has a four-layer structure. We adopt the orthogonal wavelet functions as its node functions. An online learning algorithm, structure learning and parameter learning, allows the dynamic determining of the number of wavelet bases, and adjusting the shape of the wavelet bases and the connection weights. The SCWN controller is a highly autonomous system. Initially, there are no hidden nodes. They are created and begin to grow as learning proceeds. Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme. (C) 2008 Elsevier B. V. All rights reserved.

引用

页码：71 / 79

页数：9

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