棉纺织技术2024，Vol.52Issue(4)：8-15,8.
基于ISSA-BP神经网络的棉纱条干均匀度预测
Prediction of cotton yarn evenness based on ISSA-BP neural network
摘要
Abstract
In order to solve the problem that the evenness of cotton yarn was difficult to predict,a prediction method of BP neural network optimized by improved sparrow search algorithm(ISSA)was proposed.Firstly,12 raw cotton indexes collected during the cotton yarn forming process were extracted as the input variables of BP neural network prediction model.Then,the sparrow search algorithm(SSA)was improved by using the good point set strategy,Levy flight strategy and tournament learning strategy.Finally,the ISSA-BP neural network model was established by using ISSA to search the optimal initial weights and thresholds of BP neural network.In order to verify the effectiveness of the improved algorithm,Python was used for training and simulation.The predicted results were compared with BP model,GA-BP model,PSO-BP model and SSA-BP model.The results showed that the mean relative error in cotton evenness prediction with ISSA-BP model was 1.52%.The prediction performance of ISSA-BP was the best,the error was the smallest and the prediction result was the most ideal,which could effectively predict the evenness of cotton yarn.关键词
条干均匀度预测/改进麻雀搜索算法/BP神经网络/特征提取/Python仿真Key words
prediction of evenness/improved sparrow search algorithm/BP neural network/feature extraction/Python simulation分类
轻工业引用本文复制引用
韩蔚然,俞博,方辽辽,徐郁山,陈炜..基于ISSA-BP神经网络的棉纱条干均匀度预测[J].棉纺织技术,2024,52(4):8-15,8.基金项目
浙江省科技计划项目(2022C01202) （2022C01202）
