灰色关联聚类预测分析模型研究

Li Dong, Kong Li-fang, Zhao Ying
{"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}
引用次数: 2

摘要

介绍了一种基于最小距离聚类分析的灰色关联度预测模型。针对汽车发动机性能参数子指标过多的问题,采用聚类分析实现降维。将该模型与汽车发动机的特性性能相结合,得到发动机的性能状态程度,用于监测发动机的性能。该方法能及时发现发动机潜在的前兆故障,防止故障的扩散。结果表明,该预测模型具有较高的精度和较小的误差,优于单一模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信