Optimization of Intelligent Neural Network Prediction Based on Particle Swarm

In view of the disadvantages of slow convergence and local optimality in traditional neural network learning algorithms, a neural network nonlinear function fitting system with improved particle swarm optimization (PSO) is designed for intelligent prediction accuracy. The intelligent optimization scheme integrates the immune PSO algorithm with BP neural network theory, and realizes the nonlinear function fitting algorithm of BP neural network, based on IPSO algorithm optimization. The new fitting algorithm first determines the structure of the BP neural network, and optimizes the initial weight and threshold with IPSO algorithm. Then, it improves the intelligent prediction method to verify the performance. Numerical experiments show that the algorithm proposed in this paper improves the fitting ability of BP neural network, reduces the fitting error and improves the fitting accuracy.