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

作     者：Huang, Yue Zhou, Xiaomin 

作者机构：Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China 

出 版 物：《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期：2022年第130卷

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China U1760106 

主　　题：Uncertain sampling strategy PSO algorithm RBF neural network Crown prediction 

摘      要：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.(c) 2022 Elsevier B.V. All rights reserved.