{"title":"Generalized canonical variate analysis of nonlinear systems","authors":"W. Larimore","doi":"10.1109/CDC.1988.194622","DOIUrl":null,"url":null,"abstract":"The canonical variate analysis (CVA) is extended to general nonlinear systems. Nonlinear canonical variables are shown to determine the optimum nonlinear transformation of the past maximizing the mutual information between the true and an approximating normal distribution. A sequential procedure for selection of the canonical variables is described. Nonlinear CVA is applied to nonlinear controlled Markov processes to obtain approximating nonlinear filters. A recursive innovations representation is given for the nonlinear filter that also yields an innovations representation for the Markov process model.<<ETX>>","PeriodicalId":113534,"journal":{"name":"Proceedings of the 27th IEEE Conference on Decision and Control","volume":"744 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1988.194622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
The canonical variate analysis (CVA) is extended to general nonlinear systems. Nonlinear canonical variables are shown to determine the optimum nonlinear transformation of the past maximizing the mutual information between the true and an approximating normal distribution. A sequential procedure for selection of the canonical variables is described. Nonlinear CVA is applied to nonlinear controlled Markov processes to obtain approximating nonlinear filters. A recursive innovations representation is given for the nonlinear filter that also yields an innovations representation for the Markov process model.<>
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
