Delay-dependent dissipativity criteria for Markovian jump neural networks with random delays and incomplete transition probabilities

In this paper, a new double integral inequality which covers the well-known Wirtinger's double integral inequality has been developed to analyze the dissipativity behavior of continuous-time neural networks involving Markovian jumping parameters with some unknown transition probabilities and random delays. Based on this generalized double integral inequality, the dissipativity conditions are proposed in terms of linear matrix inequalities by constructing an appropriate Lyapunov-Krasovskii functional with some multiple integral terms under the consideration of free-matrix-based integral inequality and Finsler's lemma approach. Finally, the effectiveness and the advantages of the proposed technique have been exhibited through numerical simulations.