Research on strip crown by uncertain sampling strategy modified particle swarm optimization with RBF neural network

Abstract

The strip crown directly affects the quality of strip. The prediction of strip crown by general machine learning models usually focuses on the production of a single category of strip steel, and the models lack good prediction ability for complex industrial data environments, which will lead to an increase in production costs and a decrease in product quality. Therefore, this paper proposes a new Radial Basis Function (RBF) neural network model, which is optimized by a new uncertain sampling strategy modified PSO algorithm (US-MPSO algorithm). The main feature of the algorithm is to add a new clustering dimension to the particle swarm algorithm (PSO). In this paper, clustering optimization is combined with uncertain sampling strategy to help particle swarm optimization iteration. On the one hand, the algorithm collects the population information of the strip crown as much as possible to help the neural network to converge. On the other hand, it adopts the uncertain sampling strategy to realize the optimal iteration of the samples. Finally, it realizes the improvement of the running speed and accuracy of the algorithm. This paper uses a specific model initialization strategy and combination strategy to combine the optimization algorithm with the RBF neural network. In this paper, four different types of data sets are tested to prove the adaptability of the model for complex industrial environments. The results show that the RBF neural network optimized by the US-MPSO algorithm has better prediction accuracy than the previous RBF neural network. The optimization effect of the algorithm is very significant.