Time-series prediction using a local linear wavelet neural network

被引：252
|
作者：
Chen, YH [1 ]
Yang, B
Dong, JW
机构：
[1] Jinan Univ, Sch Informat Sci & Engn, Jinan 250022, Peoples R China
[2] Wuhan Univ Sci & Technol, State Key Lab Adv Technol Mat Synthesis & Proc, Wuhan, Peoples R China
来源：
NEUROCOMPUTING | 2006年 / 69卷 / 4-6期
关键词：
local linear wavelet neural networks; particle swarm optimization algorithm; gradient descent algorithm; time-series prediction;
D O I：
10.1016/j.neucom.2005.02.006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：449 / 465
页数：17
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