Abstract
In this paper, the problem of dissipativity and passivity analysis for uncertain discrete-time stochastic Markovian jump neural networks with additive time-varying delays is investigated. By introducing a triple-summable term in the Lyapunov functional and by applying stochastic analysis technique, the dissipativity and passivity criteria are established for discrete-time neural networks with additive time-varying delays. The reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The proposed criteria that depend on the upper bounds of the additive time-varying delays are given in terms of linear matrix inequalities, which can be solved by MATLAB LMI Control Toolbox. Two numerical examples are given to demonstrate the effectiveness of the proposed method.
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