Multi-model LPV approach to CSTR system identification with stochastic scheduling variable
© 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 ident...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/51969 |
| _version_ | 1848758811500740608 |
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| author | Wei, J. Yin, YanYan Liu, F. |
| author_facet | Wei, J. Yin, YanYan Liu, F. |
| author_sort | Wei, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T09:49:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-51969 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:49:55Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-519692017-09-13T15:40:24Z Multi-model LPV approach to CSTR system identification with stochastic scheduling variable Wei, J. Yin, YanYan Liu, F. © 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. 2016 Conference Paper http://hdl.handle.net/20.500.11937/51969 10.1109/CAC.2015.7382515 restricted |
| spellingShingle | Wei, J. Yin, YanYan Liu, F. Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title | Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title_full | Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title_fullStr | Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title_full_unstemmed | Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title_short | Multi-model LPV approach to CSTR system identification with stochastic scheduling variable |
| title_sort | multi-model lpv approach to cstr system identification with stochastic scheduling variable |
| url | http://hdl.handle.net/20.500.11937/51969 |