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首页> 外文期刊>Journal of Computers >Adaptive Chaotic Prediction Algorithm of RBF Neural Network Filtering Model based on Phase Space Reconstruction
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Adaptive Chaotic Prediction Algorithm of RBF Neural Network Filtering Model based on Phase Space Reconstruction

机译：基于相空间重建的RBF神经网络滤波模型自适应混沌预测算法

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摘要

—With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are expressed. The network system can enhance the stabilization and associative memory of chaotic dynamics and generalization ability of predictive model even by imperfect and variation inputs during the learning and prediction process by selecting the suitable nonlinear feedback term. The dynamics of network become chaotic one in the weight space. Simulation experiments of chaotic time series produced by Lorenz equation are proceeded by a RBF neural network.The experimental and simulating results indicated that the forecast method of the adaptive RBF neutral network compared with the forecast method of back propagation (BP) neutral network based on the chaotic learning algorithm has faster learning capacity and higher accuracy of forecast．The method provides a new way for the chaotic time series prediction.
机译：- 本文介绍了基于径向基函数（RBF）神经网络的相位空间重构技术的分析，对混沌时间序列进行了径向基函数（RBF）神经网络。利用自适应RBF神经网络建立了混沌时间序列的预测模型，表达了具有自适应RBF神经网络的混沌学习算法的步骤。通过在学习和预测过程中选择合适的非线性反馈项，网络系统即使在学习和预测过程中的不完全和变化输入，网络系统也可以增强混沌动力学和泛化能力的稳定性和关联能力。网络的动态变得在重量空间中的混乱。 ROLENZ方程产生的混沌时间序列的模拟实验是由RBF神经网络进行的。实验和模拟结果表明，基于“的后传播（BP）中立网络的预测方法，自适应RBF中性网络的预测方法混沌学习算法具有更快的学习能力和更高的预测准确性。该方法为混沌时间序列预测提供了一种新的方式。

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