Browsing SUNY Maritime College by Subject "Neural networks"
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Risk Sensitive Optimal Synchronization of Coupled Stochastic Neural Networks with Chaotic PhenomenaThis 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.