测试科学与仪器2023，Vol.14Issue(1)：74-84,11.DOI:10.3969/j.issn.1674-8042.2023.01.009
蚁群BP神经网络在云制造知识服务组合优化中的应用
Application of ant colony BP network in composition optimization of cloud manufacturing knowledge service
摘要
Abstract
For the purpose of service composition optimization of knowledge resources for complex parts in cloud manufacturing environment,a service composition optimization model with quality of service(QoS)as optimization objective is established.Firstly,gray relational analysis is used to preprocess manufacturing resources,reduce search range of knowledge resources and reduce search cost.Then,the improved ant colony algorithm is used to optimize the knowledge resources globally to improve matching speed.Finally,the ant colony back-propagation(BP)neural network algorithm is used to improve the learning efficiency and accuracy of knowledge service composition by optimizing the optimal solution in solution space again.The experimental results show that the usage of gray relational analysis,improved ant colony algorithm,and BP neural network can reduce the search time of knowledge service,improve the matching accuracy,and effectively solve the problem of knowledge service composition optimization.关键词
柴油机/云制造/灰色关联分析/蚁群BP网络/知识服务组合优化Key words
diesel engine/cloud manufacturing/gray relation analysis(GRA)/ant colony back-propagation(BP)network/knowledge service composition optimization引用本文复制引用
蔡安江,王艺,郭师虹,潘伟..蚁群BP神经网络在云制造知识服务组合优化中的应用[J].测试科学与仪器,2023,14(1):74-84,11.基金项目
National Natural Science Foundation of China(No.51475352) （No.51475352）
Key Project of Basic Research Plan of Natural Science of Shaanxi Province(No.2019JZ-50) （No.2019JZ-50）
