{"title":"使用自组织映射监视工业过程","authors":"J. Ahola, E. Alhoniemi, O. Simula","doi":"10.1109/SMCIA.1999.782702","DOIUrl":null,"url":null,"abstract":"In this paper, three process monitoring methods based on the self-organizing map (SOM) are presented: trajectory display, fuzzy response and probabilistic response. These approaches are compared with each other and also demonstrated in two case studies: continuous pulping and hot rolling of steel strips.","PeriodicalId":222278,"journal":{"name":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Monitoring industrial processes using the self-organizing map\",\"authors\":\"J. Ahola, E. Alhoniemi, O. Simula\",\"doi\":\"10.1109/SMCIA.1999.782702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, three process monitoring methods based on the self-organizing map (SOM) are presented: trajectory display, fuzzy response and probabilistic response. These approaches are compared with each other and also demonstrated in two case studies: continuous pulping and hot rolling of steel strips.\",\"PeriodicalId\":222278,\"journal\":{\"name\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMCIA.1999.782702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.1999.782702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monitoring industrial processes using the self-organizing map
In this paper, three process monitoring methods based on the self-organizing map (SOM) are presented: trajectory display, fuzzy response and probabilistic response. These approaches are compared with each other and also demonstrated in two case studies: continuous pulping and hot rolling of steel strips.