Robust dissipativity analysis for stochastic Markov jump competitive neural networks with mixed delays

This research addresses the critical issue of dissipativity in Markovian jump stochastic competitive neural networks, particularly focusing on the complex challenges posed by mixed time delays and parameter uncertainties. The primary objective of this study is to derive adequate conditions for dissipativity by constructing suitable Lyapunov–Krasovskii functionals (LKFs) that incorporates triple integral terms, thereby providing a rigorous mathematical framework for analysis. To achieve this, a generalized delay-dependent reciprocal convex inequality is employed, which enables us to effectively calculate the derivative of the LKFs and derive a linear matrix polynomial. Our findings extend the conventional dissipativity criteria to include passivity, which is articulated in terms of linear matrix inequalities (LMIs). This enhancement significantly simplifies the computational process and allows for practical implementation using standard numerical software. Further, Numerical examples demonstrate that the proposed strategy outperforms existing findings.