基于K-近邻算法改进粒子群-反向传播算法的织物质量预测技术

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中文摘要：为解决现有下机织物质量差异性较大且传统验布环节时间较长等问题,提出基于K-近邻(KNN)算法改进粒子群-反向传播(PSO-BP)算法的织物质量等级预测方法.首先分析织物质量预测模型,整理织物疵点类型与织物质量等级分类,并根据织物疵点特征将疵点划分为 6 类;其次选取 14 种影响织物质量的因子作为模型输入量;然后详细介绍依据KNN与PSO原理进行织物质量预测流程;最后以浙江兰溪某纺织厂近 3 个月16 186 条织物生产数据为例,建立织物质量预测模型.结果显示:该技术对织物质量预测的准确率达到 98.054%,且训练时长仅需 4.8 s,在保证织物质量预测准确性的同时,极大缩短了检测时间,提高了织造车间生产效率.

外文标题：Fabric quality prediction technology based on K-nearest neighbor algorithm improved particle swarm optimization-back propagation algorithm

外文摘要：Objective The confirmation of fabric quality in the textile industry is usually to put the woven fabric into the inspection equipment for inspection.When the fabric defects are found in the inspection process,the repair will be carried out and so increases the production time,thereby reducing the workshop efficiency.In order to improve the efficiency of the workshop,by collecting the real-time data of the weaving workshop,the fabric quality prediction model is established to predict the fabric quality and reduce the fabric production time.Method Aiming at the problem of large difference in fabric quality and long time of conventional fabric inspection,a fabric quality grade prediction method based on K-nearest neighbor algorithm(KNN)improved PSO-BP algorithm was proposed by combining KNN and particle swarm optimization(PSO)improved error back propagation(BP)neural network algorithm.Firstly,the fabric quality prediction model is analyzed,and the fabric defects and fabric quality grades are divided.Secondly,14 factors affecting the fabric quality are selected as the model input,and then the KNN algorithm is adopted to classify the original sample set.Finally,the classified data is brought into the fabric quality prediction model.The fabric quality prediction model is to use the particle swarm optimization algorithm to obtain the position and speed of the optimal solution through iterative update,and take this as the initial weight and threshold into the neural network structure for training to obtain the model.By predicting the fabric quality grade,the fabric quality is improved.Results 16,186 fabric production data collected over a 3-month period from a textile factory in Lanxi,Zhejiang Province were adopted to establish a fabric quality prediction model.Firstly,the original data set was adopted to compare and analyze PSO-BP and BP algorithms with different training target errors.According to results of KNN-PSO-BP netural network model,PSO-BP algorithm showed higher accuracy and higher training speed than BP algorithm,and PSO-BP neural network model demonstrated an accuracy of 96%with the training target error 0.000 1.The KNN algorithm was adopted to divide the original sample set into five categories.The mean square error,accuracy and training time of the neural network model were calculated when the training target error is 0.000 1.The accuracy of the KNN-PSO-BP neural network model was 98.054%.Conclusion This research demonstrated that KNN-PSO-BP algorithm has higher accuracy than PSO-BP algorithm and BP algorithm.The training time of fabric quality grade prediction is only 4.8 s,and the accuracy rate is 98.054%.The algorithm greatly shortens the detection time while ensuring the accuracy of fabric quality prediction,improves the production efficiency of weaving,and provides a certain basis for subsequent research on the location and size of fabric defects.

外文关键词：

weaving workshopfabric qualityK-nearest neighbor algorithmparticle swarm optimization-back propagation neural network algorithmfabric quality prediction

作者：

孙长敏、戴宁、沈春娅、徐开心、陈炜、胡旭东、袁嫣红、陈祖红

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作者单位：

浙江理工大学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

关键词：

织布车间织物质量 K-近邻算法粒子群-反向传播神经网络算法织物质量预测

基金：

浙江省博士后科研项目浙江省"尖兵""领雁"研发攻关计划资助项目浙江理工大学科研启动基金项目

项目编号：

ZJ20210382022C010652022C0120223242083-Y

出版年：

2024

DOI：

10.13475/j.fzxb.20230307001

纺织学报

中国纺织工程学会

纺织学报

CSTPCD北大核心

影响因子：0.699

ISSN：0253-9721

年,卷(期)：2024.45(7)