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 |
| Summary: | © 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. |
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