{"title":"灰色关联聚类预测分析模型研究","authors":"Li Dong, Kong Li-fang, Zhao Ying","doi":"10.1109/IHMSC.2012.118","DOIUrl":null,"url":null,"abstract":"This paper introduces a model of grey incidence prediction based on minimum distance cluster analysis. Due to the fact that there are too many sub-indexes for performance parameter of automobile engine, cluster analysis is conducted to realize dimension reduction. This model was combined with characteristic performance of automobile engine to attain the degrees of engine performance's state for monitoring engine performance. This method finds out the potential forepart fault of engine and prevents the spread of the fault. The result indicates that, the prediction model is better than that of the single models for higher precision and smaller error.","PeriodicalId":431532,"journal":{"name":"2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research of Grey Incidence Cluster Prediction Analysis Model\",\"authors\":\"Li Dong, Kong Li-fang, Zhao Ying\",\"doi\":\"10.1109/IHMSC.2012.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a model of grey incidence prediction based on minimum distance cluster analysis. Due to the fact that there are too many sub-indexes for performance parameter of automobile engine, cluster analysis is conducted to realize dimension reduction. This model was combined with characteristic performance of automobile engine to attain the degrees of engine performance's state for monitoring engine performance. This method finds out the potential forepart fault of engine and prevents the spread of the fault. The result indicates that, the prediction model is better than that of the single models for higher precision and smaller error.\",\"PeriodicalId\":431532,\"journal\":{\"name\":\"2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2012.118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2012.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research of Grey Incidence Cluster Prediction Analysis Model
This paper introduces a model of grey incidence prediction based on minimum distance cluster analysis. Due to the fact that there are too many sub-indexes for performance parameter of automobile engine, cluster analysis is conducted to realize dimension reduction. This model was combined with characteristic performance of automobile engine to attain the degrees of engine performance's state for monitoring engine performance. This method finds out the potential forepart fault of engine and prevents the spread of the fault. The result indicates that, the prediction model is better than that of the single models for higher precision and smaller error.