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    Multi-model LPV approach to CSTR system identification with stochastic scheduling variable

    Access Status
    Fulltext not available
    Authors
    Wei, J.
    Yin, YanYan
    Liu, F.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Wei, J. and Yin, Y. and Liu, F. 2016. Multi-model LPV approach to CSTR system identification with stochastic scheduling variable, pp. 303-307.
    Source Title
    Proceedings - 2015 Chinese Automation Congress, CAC 2015
    DOI
    10.1109/CAC.2015.7382515
    ISBN
    9781467371896
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/51969
    Collection
    • Curtin Research Publications
    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.

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