Regression prediction of material grinding particle size based on improved sparrow search algorithm to optimize BP neural network

Aiming at the problem that the common material grinding factors in the industry are complex and it is difficult to accurately predict the output particle size, this paper introduces the sparrow search algorithm, and proposes two improved strategies for the sparrow search algorithm. For the original sparrow search algorithm, the global search ability is insufficient. And the problem that is easy to fall into the local optimum, the introduction of Tent chaotic map to initialize the population, enhance the global search ability. Meanwhile, introduce the Cauchy mutation strategy to solve the local optimum problem, effectively improve the algorithm search ability, and combine the BP neural network to grind the output particle size of the material make predictions. The simulation results show that the improved sparrow search algorithm optimizes the weights and biases of the BP neural network and improves the training accuracy of the BP neural network. The experimental results show that the proposed TCSSA-BP model has obvious effects on the regression prediction of the output particle size of the material grinding.