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    RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

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    Publication date
    2020-01
    Author
    Yin, X.
    Zhang, Qichun
    Wang, H.
    Ding, Z.
    Keyword
    Minimum entropy filtering
    Stochastic nonlinear systems
    Non-Gaussian distribution
    Mathematical modelling
    Neural networks
    Rights
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
    Peer-Reviewed
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    Abstract
    This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.
    URI
    http://hdl.handle.net/10454/17339
    Version
    Accepted manuscript
    Citation
    Yin X, Zhang Q, Wang H et al (2020) RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems. IEEE Transactions on Automatic Control. 65(1): 376-381.
    Link to publisher’s version
    https://doi.org/10.1109/TAC.2019.2914257
    Type
    Article
    Collections
    Engineering and Informatics Publications

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