Abstract
For the multi-step prediction object of traffic flow chaotic time series, a fast learning algorithms of VNNTF based on chaotic mechanism was proposed. First, combination of chaotic phase space reconstruction properties to traffic flow chaotic time series, method of the truncation order and the truncation items is given, and the VNNTF neural networks model was build by this. Second, based on chaotic learning algorithm, and designed neural network traffic Volterra learning algorithm for fast learning algorithm. Last, a multi-step prediction of traffic flow chaotic time series is researched by VNNTF network model, Volterra prediction filter and the BP neural network based on chaotic algorithm. The results showed that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network by the simulation results and root-mean-square value.
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Yin, L., He, Y., Dong, X., Lu, Z. (2012). Multi-step Prediction of Volterra Neural Network for Traffic Flow Based on Chaos Algorithm. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_32
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DOI: https://doi.org/10.1007/978-3-642-34038-3_32
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