中国物理B ›› 2008, Vol. 17 ›› Issue (6): 1998-2003.doi: 10.1088/1674-1056/17/6/011

• GENERAL • 上一篇    下一篇

Time series prediction using wavelet process neural network

丁刚, 钟诗胜, 李洋   

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
  • 收稿日期:2007-10-06 修回日期:2007-10-29 出版日期:2008-06-20 发布日期:2008-06-20
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No 60572174), the Doctoral Fund of Ministry of Education of China (Grant No 20070213072), the 111 Project (Grant No B07018), the China Postdoctoral Science Foundation (Grant No 2

Time series prediction using wavelet process neural network

Ding Gang(丁刚)†, Zhong Shi-Sheng(钟诗胜), and Li Yang(李洋)   

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
  • Received:2007-10-06 Revised:2007-10-29 Online:2008-06-20 Published:2008-06-20
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No 60572174), the Doctoral Fund of Ministry of Education of China (Grant No 20070213072), the 111 Project (Grant No B07018), the China Postdoctoral Science Foundation (Grant No 2

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摘要： In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Mackey--Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.

Abstract: In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Mackey--Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.

Key words: time series, prediction, wavelet process neural network, learning algorithm

中图分类号:  (Time series analysis)

  • 05.45.Tp
07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)