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Risk Sensitive Optimal Synchronization of Coupled Stochastic Neural Networks with Chaotic Phenomena
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2015-05-26
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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.
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Z. Liu, “Risk Sensitive Optimal Synchronization of Coupled Stochastic Neural Networks with Chaotic Phenomena,” 2015.
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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.