Optimal Design of Hydrodynamic Journal Bearing Based on BP Neural Network Optimized by Improved Particle Swarm Algorithm

In this paper, an algorithmic model of hydrodynamic journal bearing with the goal of optimizing particle algorithm is set up, and particle algorithm is optimized by combining artificial neural network. The effect of particle algorithm coupled with neural network on the optimized design results of hydrodynamic journal bearings is studied, and the computational flow of the coupled two algorithms is proposed. The calculation example shows that the selection of the parameters of neural network based on the improved algorithm majorization in hydrodynamic journal bearings proposed in the paper is reasonable and can be extended to other fields of optimization design based on particle swarm algorithm.