A Self-Organizing Recurrent Wavelet Neural Network for Nonlinear Dynamic System Identification

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/5965

Title:	A Self-Organizing Recurrent Wavelet Neural Network for Nonlinear Dynamic System Identification
Authors:	Cheng-Jian Lin, Chun-Cheng Peng , Cheng-Hung Chen and Hsueh-Yi Lin
Contributors:	圖書館
Keywords:	Recurrent neural network wavelet bases identification online learning backpropagation degree measure
Date:	2015
Issue Date:	2016-10-20 15:09:59 (UTC+8)
Abstract:	: To solve identification of nonlinear dynamic systems, a recurrent wavelet neural network (RWNN) model is proposed in this paper. The proposed RWNN model has four-layer structure. Temporal relations embedded in the network by adding some feedback connections representing the memory units in the second layer. An online learning algorithm, which consists of structure learning and parameter learning, is proposed and is able to construct the wavelet neural network dynamically. The structure learning is based on the input partitions to determine the number of wavelet bases, and the parameter learning is based on the supervised gradient descent method to adjust the shape of wavelet functions, feedback weights, and the connection weights. Computer simulations were conducted to illustrate the performance and applicability of the proposed model.
Relation:	Applied Mathematics & Information Sciences, Appl. Math. Inf. Sci. 9, No. 1L, 125-132
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

Files in This Item:

File	Description	Size	Format
A Self-Organizing Recurrent Wavelet Neural Network for Nonlinear Dynamic System Identification.pdf		291Kb	Adobe PDF	1213	View/Open

View Licence