The Research of Neural Network Prediction Based on the GEP

Change the BP Algorithm rely on the gradient information to adjust the network weights, the optimal gene sequences decode the expression tree so get the best neural network structure and the evolution of weights and thresholds, so that the structure of artificial neural network and weights can be optimized at the same time. This method gives the neural network mapping capabilities and GEP's ability to solve complex problems, accelerate the learning speed of the network, improve the approximation ability and generalization ability. GEP-BP will be used in Shanghai Composite Index forecast. Experimental results show that this method improved the prediction accuracy and achieved a better prediction.