| A Novel Method for Nonlinear Time Series Forecasting of Time-Delay Neural Network |
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| 作者姓名： | JIANG Weijin XU Yuhui |
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| 作者单位： | [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 |
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| 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.
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| 关 键 词： | 非线性预测 相空间重建 BP网络 贝叶斯规则化 神经网络 |
| 文章编号： | 1007-1202（2006）05-1357-05 |
| 收稿时间： | 2006-03-01 |
A novel method for nonlinear time series forecasting of time-delay neural network |
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| JIANG Weijin XU Yuhui.A Novel Method for Nonlinear Time Series Forecasting of Time-Delay Neural Network[J].Wuhan University Journal of Natural Sciences,2006,11(5):1357-1361. |
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| Authors: | Jiang Weijin Xu Yuhui |
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| Institution: | (1) Department of Computer, Hunan University of Technology, 412008 Zhuzhou, Hunan, China;(2) Department of Information and Computer, Hunan University of Technology, 412008 Zhuzhou, Hunan, China |
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| Abstract: | 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 forecasting 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. |
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| Keywords: | nonlinear prediction phase space reconstruction BP Bayesian regularization |
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