Chaotic Time Series Forecasting with PSO-Trained RBF Neural Network

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.

关键词： RBF Network Training algorithm Particle swarm Chaotic Time Series

作者: Bin Feng Wei Chen Jun Sun

作者单位: School of Information Technology, Southern Yangtze University No.1800, Lihudadao Road, Wuxi, 214122, Jiangsu, China

会议类型: 国际会议

会议名称: 2006 International Symposium on Distributed Computing and Applications to Business,Engineering and Science(2006年国际电子、工程及科学领域的分布式计算应用学术研讨会)

会议地点: 杭州

会议语种:英文

页码: 787-790

在线出版日期: 2006-10-12（万方平台首次上网日期，不代表论文的发表时间）