{"title":"非常数参数马尔可夫模型的逼近","authors":"U. Desai, Saibal Banerjee, S. Kiaei","doi":"10.1109/CDC.1984.272381","DOIUrl":null,"url":null,"abstract":"A generalization of the canonical correlation analysis approach has been developed for non-stationary process generated by Markovian models with non-constant parameters. This generalization, is then used to develop two model reduction (approximation) algorithms.","PeriodicalId":269680,"journal":{"name":"The 23rd IEEE Conference on Decision and Control","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approximation of Markovian models with non-constant parameters\",\"authors\":\"U. Desai, Saibal Banerjee, S. Kiaei\",\"doi\":\"10.1109/CDC.1984.272381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A generalization of the canonical correlation analysis approach has been developed for non-stationary process generated by Markovian models with non-constant parameters. This generalization, is then used to develop two model reduction (approximation) algorithms.\",\"PeriodicalId\":269680,\"journal\":{\"name\":\"The 23rd IEEE Conference on Decision and Control\",\"volume\":\"118 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 23rd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1984.272381\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 23rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1984.272381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Approximation of Markovian models with non-constant parameters
A generalization of the canonical correlation analysis approach has been developed for non-stationary process generated by Markovian models with non-constant parameters. This generalization, is then used to develop two model reduction (approximation) algorithms.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
