棉纺织技术2024，Vol.52Issue(4)：1-7,7.
基于ISSA-BP神经网络的纺纱生产工艺参数反演
Parameter inversion of spinning production process based on ISSA-BP neural network
摘要
Abstract
In order to solve the problems of lower accuracy of traditional inversion model,excessive randomization of weights and initial threshold values,worse stability and accuracy of traditional BP(back propagation)neural network,a parameter inversion model for spinning production process of BP neural network based on improved sparrow search algorithm(ISSA)was proposed.Ten key process parameters were extracted by grey relational analysis and used as model input.Chebyshev chaotic map,sine cosine algorithm(SCA)and adaptive weight factor were introduced to optimize sparrow search algorithm(SSA).And BP neural network was optimized by the improved sparrow search algorithm(ISSA).On this basis,the parameter inversion model of spinning production process was constructed.ISSA was used to solve the parameter inversion model.Inversion verification was carried out with fiber property and spinning workshop as objects.The experimental results showed that the MAPE,MSE,MAE,iteration time and fitness of ISSA-BP predicted values were all better than those of SSA-BP model.The process parameters after inversion optimization were forecasted.The mean relative error(MRE)between predicted quality index and expected quality index was 5.04%.It is considered that the inversion precision of spinning process parameters based on ISSA-BP neural network is higher,which is helpful to the reasonable design of process parameters.关键词
纺纱生产/工艺参数反演优化/纱线质量/麻雀搜索算法/神经网络Key words
spinning production/process parameter inversion optimization/yarn quality/sparrow search algorithm/neural network分类
信息技术与安全科学引用本文复制引用
刘颖,张守京,胡胜..基于ISSA-BP神经网络的纺纱生产工艺参数反演[J].棉纺织技术,2024,52(4):1-7,7.基金项目
西安市现代智能纺织装备重点实验室(2019220614SYS021CG043) （2019220614SYS021CG043）
