Prediction of Chaotic Time Series Based on Incremental Method For Bayesian Network Learning

摘要：

The prediction of Chaotic time series constitutes a hot research topic of chaos theory, it is widely used in signal processing and automatic control field. In order to sufficiently model time series, on the base of the theory of phase-space reconfiguration, using the advantages of incremental method for Bayesian network learning in dealing the uncertainty to build a nonlinear prediction model for the prediction of chaotic time series. The method is applied to a chaotic time series produced by Henon equation, and the experimental results show that our prediction models has better predictability and stability than K2 algorithm and SVD predictive models.

关键词： Chaotic time series Phase space reconfiguration Bayesian network Incremental learning Prediction

作者: LI Chun-ying YANG You-long ZHANG Heng-wei

作者单位: School of Science, Xidian University, Xi’an 710071, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 4245-4249

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）