Adaptive RBF Neural Network Filtering Predictive Model Based on Chaotic Algorithm

In this paper, based on the RBF neural networks and the deterministic and nonlinear characterization of chaotic time series, the adaptive RBF neural network filtering predictive model based on chaotic algorithm is proposed to make prediction of chaotic time series. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm 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 model is tested for the chaotic time series which venerated with Lorentz system by on-line method. The experimental and simulating results indicated that the adaptive RBF neural network filtering predictive model has a good adaptive prediction performance and can be successfully used to predict chaotic time series.