Research ArticleRobust stochastic stability of discrete-time fuzzy Markovian jump neural networks

- Applications of neural networks require the knowledge of dynamic behaviors.
- Stochastic stability of discrete-time fuzzy neural networks is studied.
- Linear matrix inequality optimization approach is used to obtain the result.
- Delay dependent stability criterion is established in terms of LMIs.
- Examples with simulation are provided to show the effectiveness of the result.

This paper focuses the issue of robust stochastic stability for a class of uncertain fuzzy Markovian jumping discrete-time neural networks (FMJDNNs) with various activation functions and mixed time delay. By employing the Lyapunov technique and linear matrix inequality (LMI) approach, a new set of delay-dependent sufficient conditions are established for the robust stochastic stability of uncertain FMJDNNs. More precisely, the parameter uncertainties are assumed to be time varying, unknown and norm bounded. The obtained stability conditions are established in terms of LMIs, which can be easily checked by using the efficient MATLAB-LMI toolbox. Finally, numerical examples with simulation result are provided to illustrate the effectiveness and less conservativeness of the obtained results.