{"title":"Risk-sensitive optimal control for stochastic recurrent neural networks","authors":"Ziqian Liu, R. E. Torres, Miltiadis Kotinis","doi":"10.1109/MWSCAS.2010.5548858","DOIUrl":null,"url":null,"abstract":"As a continuation of our study, this paper extends our research results of optimality-oriented control from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new theoretical design for the risk-sensitive optimal control of stochastic recurrent neural networks. The design procedure follows the technique of inverse optimality, and obtains risk-sensitive state feedback controllers that guarantee an achievable meaningful cost for a given risk-sensitivity parameter.","PeriodicalId":245322,"journal":{"name":"2010 53rd IEEE International Midwest Symposium on Circuits and Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 53rd IEEE International Midwest Symposium on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2010.5548858","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
As a continuation of our study, this paper extends our research results of optimality-oriented control from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new theoretical design for the risk-sensitive optimal control of stochastic recurrent neural networks. The design procedure follows the technique of inverse optimality, and obtains risk-sensitive state feedback controllers that guarantee an achievable meaningful cost for a given risk-sensitivity parameter.