Optimal Chaos Sequence Prediction Based on Optimized Radial Basis Function Neural Network

In order to better predict chaotic time series, this paper proposes a normalized detrended spatiotemporal radial basis function network (ND-ST-RBFNN) based on the traditional radial basis function neural network (RBFNN) and the spatiotemporal radial basis function network (ST-RBFNN). The input data is normalized and detrended using a linear regression model before entering the network. This method is used to predict the Chebyshev chaotic time series. Compared to the traditional ST-RBFNN, this method reduces the mean squared error of the predictions and improves prediction accuracy. Experimental results illustrate the efficacy of this approach, showing good performance and flexibility in chaotic time series prediction problems.