关键词: 神经网络, 粒子群算法, 工艺参数优化

Abstract: A process parameter optimization strategy for reducing silicon steel iron loss was proposed by combining BP neural network and particle swarm optimization (PSO) algorithm. Firstly, a BP neural network was used to establish a prediction model for silicon steel iron loss, which has high fitting and prediction accuracy. Then, in terms of optimizing process parameters, the BP neural network prediction model was used as the fitness function, and the furnace temperatures of each section of the continuous annealing RTF furnace were selected as optimization variables. PSO algorithm was used to optimize these process parameters. The results showed that using BP neural network and PSO algorithm to optimize some process parameters can significantly reduce the iron loss of silicon steel, which has certain guiding significance.

Key words: neural network, particle swarm optimization (PSO) algorithm, process parameter optimization