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dc.contributor.authorWei, J.
dc.contributor.authorYin, YanYan
dc.contributor.authorLiu, F.
dc.date.accessioned2017-04-28T13:57:01Z
dc.date.available2017-04-28T13:57:01Z
dc.date.created2017-04-28T09:06:14Z
dc.date.issued2016
dc.identifier.citationWei, J. and Yin, Y. and Liu, F. 2016. Multi-model LPV approach to CSTR system identification with stochastic scheduling variable, pp. 303-307.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/51969
dc.identifier.doi10.1109/CAC.2015.7382515
dc.description.abstract

© 2015 IEEE.The problem of CSTR system identification is studied with a stochastic scheduling parameter. Multi-model approach is used to describe non-linear process, in which, each linear parameter system is represented by a ARX model. An expectation maximization (EM) algorithm is used for the identification of parameters which are unknown. Furthermore, scheduling variable corresponds to the operating conditions of the nonlinear process is considered as a stochastic parameter, which follows a Markov jump process.

dc.titleMulti-model LPV approach to CSTR system identification with stochastic scheduling variable
dc.typeConference Paper
dcterms.source.startPage303
dcterms.source.endPage307
dcterms.source.titleProceedings - 2015 Chinese Automation Congress, CAC 2015
dcterms.source.seriesProceedings - 2015 Chinese Automation Congress, CAC 2015
dcterms.source.isbn9781467371896
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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