Research on Residual Strength Prediction Model of Corroded Pipeline Based on Improved PSO-BP

Abstract In this paper, an improved particle swarm optimization (PSO) BP neural network model is proposed. For standard particle swarm optimization algorithm (PSO) is the "premature" and lack of diversity in the late, this paper puts forward an improved particle swarm optimization algorithm (IPSO), using the improved particle swarm optimization algorithm to optimize the BP neural network, which improves the learning ability of the BP neural network and accelerates its convergence speed. The IPSO-BPNN prediction model was constructed by using the BP neural network optimized by improved particle swarm optimization, and was applied to the prediction of residual strength of corroded pipeline. The experiment was carried out by the real pipeline test blasting data set. The results show that the prediction accuracy of IPSO-BPNN is better than that of BPNN and PSO-BPNN.