BP Neural Network Model Based on Phase Space Reconstruction

The BP neural network has proven robust even for complex nonlinear problems. However, its high performance results are attained at the expense of a long training time to adjust the network parameters, which can be discouraging in many real-world applications. Even on relatively simple problems, standard BP often requires a lengthy training process in which the complete set of training examples is processed hundreds or thousands of times. In this paper, a BP neural network model based on phase space reconstruction is presented. Simulation shows that the combined model has greatly enhanced efficiency and accuracy of prediction and obtained a perfect result.