Mode-dependent stochastic stability criteria of fuzzy Markovian jumping neural networks with mixed delays

- First LMI-based mode-dependent stochastic stability result of fuzzy Markovian neural networks with time-varying and unbounded distributed delays.
- Establishing a Jensen-type integral inequality for triple integral.
- Effective combination of Jensen integral inequality, the generalized Jensen integral inequality, linear convex combination technique.
- Application of linear matrix inequality technique and the free-weight matrix method.
- Less conservative LMI-based delay-dependent sufficient conditions.

This paper investigates the stochastic stability of fuzzy Markovian jumping neural networks with mixed delays in mean square. The mixed delays include time-varying delay and continuously distributed delay. By using the Lyapunov functional method, Jensen integral inequality, the generalized Jensen integral inequality, linear convex combination technique and the free-weight matrix method, several novel sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which do not require the differentiability of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.