| Modeling and Simulation of Time Series Prediction Based on Dynamic Neural Network |
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| 作者姓名： | 王雪松 程玉虎 彭光正 |
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| 作者单位： | [1]SchoolofInformationScienceandTechnology,BeijingInstituteofTechnology,Beijing100081,China [2]LaboratoryofComplexSystemandIntelligentScience,InstituteofAutomation,ChineseAcademyofSciences,Beijing100080,China |
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| 摘 要： | Molding and simulation of time series prediction based on dynamic neural network(NN) are studied.Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward.The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multistep. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.
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| 关 键 词： | 时间序列 Jordan神经网络 背向传播算法 暂态差分 |
| 收稿时间： | 6/2/2003 12:00:00 AM |
Modeling and Simulation of Time Series Prediction Based on Dynamic Neural Network |
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| WANG Xue-song,Cheng Yu-hu and PENG Guang-zheng.Modeling and Simulation of Time Series Prediction Based on Dynamic Neural Network[J].Journal of Beijing Institute of Technology,2004,13(2):148-151. |
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| Authors: | WANG Xue-song Cheng Yu-hu and PENG Guang-zheng |
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| Affiliation: | WANG Xue-song~1,Cheng Yu-hu~2,PENG Guang-zheng~1 |
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| Abstract: | Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series. |
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| Keywords: | time series Jordan neural network(NN) back-propagation (BP) algorithm temporal difference (TD) method |
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