{"title":"基于灰色免疫机制的有机制造系统状态评价","authors":"Qixiang Cai, Haihua Zhu, D. Tang, T. Huang","doi":"10.1109/GSIS.2015.7301832","DOIUrl":null,"url":null,"abstract":"Based on a comparison of manufacturing and biological organism systems, we have proposed the concept of an organic manufacturing system. To eliminate promptly uncertain disturbances during the organic manufacturing system's operation, an immune monitoring model of an organic manufacturing system, analogous to a biological immune system mechanism is proposed. The implementation of immune recognition, assessment, learning, memory and regulation in response to abnormal disturbance factors is investigated. The system's grey assessment model based on an analytic hierarchy is carefully analyzed, and the effectiveness of the system state assessment method is verified experimentally through simulation.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"System state assessment of a grey immune mechanism-based organic manufacturing system\",\"authors\":\"Qixiang Cai, Haihua Zhu, D. Tang, T. Huang\",\"doi\":\"10.1109/GSIS.2015.7301832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on a comparison of manufacturing and biological organism systems, we have proposed the concept of an organic manufacturing system. To eliminate promptly uncertain disturbances during the organic manufacturing system's operation, an immune monitoring model of an organic manufacturing system, analogous to a biological immune system mechanism is proposed. The implementation of immune recognition, assessment, learning, memory and regulation in response to abnormal disturbance factors is investigated. The system's grey assessment model based on an analytic hierarchy is carefully analyzed, and the effectiveness of the system state assessment method is verified experimentally through simulation.\",\"PeriodicalId\":246110,\"journal\":{\"name\":\"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)\",\"volume\":\"210 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2015.7301832\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2015.7301832","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
System state assessment of a grey immune mechanism-based organic manufacturing system
Based on a comparison of manufacturing and biological organism systems, we have proposed the concept of an organic manufacturing system. To eliminate promptly uncertain disturbances during the organic manufacturing system's operation, an immune monitoring model of an organic manufacturing system, analogous to a biological immune system mechanism is proposed. The implementation of immune recognition, assessment, learning, memory and regulation in response to abnormal disturbance factors is investigated. The system's grey assessment model based on an analytic hierarchy is carefully analyzed, and the effectiveness of the system state assessment method is verified experimentally through simulation.