中国物理B ›› 2008, Vol. 17 ›› Issue (6): 1998-2003.doi: 10.1088/1674-1056/17/6/011
丁刚, 钟诗胜, 李洋
Ding Gang(丁刚)†, Zhong Shi-Sheng(钟诗胜), and Li Yang(李洋)
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.
中图分类号: (Time series analysis)