Modeling and Simulation of Time Series Prediction Based on Dynic Neural Network |
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Affiliation: | School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Laboratory of Complex System and Intelligent Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China |
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Abstract: | Molding and simulation of time series prediction based on dynic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynic 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 exple 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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