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    Risk Sensitive Optimal Synchronization of Coupled Stochastic Neural Networks with Chaotic Phenomena

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    Author
    Liu, Ziqian
    Keyword
    Decision support systems
    Synchronization
    Neural networks
    State feedback
    Noise
    Sensitivity
    Optimal control
    Date Published
    2015-05-26
    
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    URI
    http://hdl.handle.net/20.500.12648/1542
    Abstract
    This paper presents a new theoretical design of how an optimal synchronization is achieved for stochastic coupled neural networks with respect to a risk sensitive optimality criterion. The approach is rigorously developed by using the Hamilton-Jacobi-Bellman equation, Lyapunov technique, and inverse optimality, to obtain a risk sensitive state feedback controller, which guarantees that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals, with an eye on a given risk sensitivity parameter. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.
    Citation
    Published in 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)
    Description
    This article was published in the 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA). Date of conference: 26-28 May 2015. DOI: 10.1109/CISDA.2015.7208632. Copyright IEEE 2015.
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