Chaotic time series forecasting with PSO-trained RBF neural network

被引：0
|
作者：
Feng, Bin [1 ]
Chen, Wei [1 ]
Sun, Jun [1 ]
机构：
[1] So Yangtze Univ, Sch Informat Technol, Wuxi 214122, Jiangsu, Peoples R China
来源：
DCABES 2006 PROCEEDINGS, VOLS 1 AND 2 | 2006年
关键词：
RBF Network; training algorithm; particle swarm; Chaotic Time Series;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Radial Basis Function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. In this paper, we use Particle Swarm Optimization (PSO), a evolutionary search technique, to train RBF neural network and therefore apply PSO-trained RBF network in chaotic time series forecasting. The proposed method was test on Mackey-Glass model, and the results show that it can predict the time series quickly and precisely.
引用
收藏
页码：787 / 790
页数：4
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