基于ISSA-BP神经网络的棉纱条干均匀度预测OACSTPCD
Prediction of cotton yarn evenness based on ISSA-BP neural network
为解决棉纱条干均匀度难以预测的问题,提出了一种改进麻雀搜索算法(ISSA)优化BP神经网络的预测方法.首先,将棉纱成形过程中采集到的12个原棉指标进行特征提取,作为BP神经网络预测模型的输入变量.接着,利用佳点集策略,Levy飞行策略和锦标赛学习策略对麻雀搜索算法(SSA)进行改进.最后,利用ISSA搜索BP神经网络最优的初始权值和阈值,建立ISSA-BP神经网络模型.为验证改进算法的有效性,利用Python进行训练和仿真,并与BP模型、GA-B…查看全部>>
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 strateg…查看全部>>
韩蔚然;俞博;方辽辽;徐郁山;陈炜
浙江理工大学,浙江杭州,310018||浙江省现代纺织装备技术重点实验室,浙江杭州,310018浙江理工大学,浙江杭州,310018||浙江省现代纺织装备技术重点实验室,浙江杭州,310018浙江理工大学,浙江杭州,310018||浙江省现代纺织装备技术重点实验室,浙江杭州,310018浙江康立自控科技有限公司,浙江绍兴,312500浙江天衡信息技术有限公司,浙江绍兴,312500
轻工业
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
prediction of evennessimproved sparrow search algorithmBP neural networkfeature extractionPython simulation
《棉纺织技术》 2024 (4)
8-15,8
浙江省科技计划项目(2022C01202)
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