A Novel Method for Nonlinear Time Series Forecasting of Time-Delay Neural Network  

作　　者：JIANG Weijin XU Yuhui 

机构地区：[1]Department of Computer, Hunan University of Technology,Zhuzhou 412008, Hunan, China [2]Department ot Information and Computer, Hunan University ofTechnology, Zhuzhou 412008, Hunan, China

出　　处：《Wuhan University Journal of Natural Sciences》2006年第5期1357-1361,共5页武汉大学学报（自然科学英文版）

基　　金：Supported bythe Natural Science Foundation of Hunan Province(2001ABB006 ,2003ABA043)

摘　　要：Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.

关 键 词：nonlinear prediction phase space reconstruction BP Bayesian regularization 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]