Prediction for chaotic time series of optimized BP neural network based on modified PSO

In order to improve forecasting model accuracy of BP neural network, an improved prediction method of optimized BP neural network based on modified particle swarm optimization algorithm (PSO) was proposed. In this modified PSO algorithm, an adaptive mutation operator was proposed in PSO to change positions of the particles plunged in the local optimization. The modified PSO was used to optimize the weights and thresholds of BP neural network, and then BP neural network was trained to search for the optimal solution. The availability of the proposed prediction method was proved by predicting several typical nonlinear systems. The simulation results have shown that the better fitting and higher accuracy are expressed in this improved method.