Short-Term Prediction on Parameter-Varying Systems by Multiwavelets Neural Network

Abstract

Numerous studies on time series prediction have been undertaken by a lot of researchers. Most of them relate to the construction of structure-invariable system whose parameter values do not change all the time. In fact, the parameter values of many realistic systems are always changing with time. In this case, the embedding theorems are invalid, predicting the behavior of parameter-varying systems is more difficult. This paper presents a new prediction technique, which is multiwavelets neural network. This technique absorbs the advantage of high resolution of wavelets and the advantages of learning and feed-forward of neural networks. The procedure of using the multiwavelets neural network for predicting is described in detail in this paper. Principal components analysis (PCA) as a statistical technique has been used to simplify the time series analysis in our experiments. The effectiveness of this network is demonstrated by applying it to predict Ikeda time series.

This work was supported by the National Science Foundation of China (Grant No.60375021) and the National Science Foundation of Hunan Province (Grant No.00JJY3096 and No.04JJ20010) and the Key Project of Hunan Provincial Education Department (Grant No.04A056).