Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters
Grienggrai Rajchakit, Ramalingam Sriraman, Rajendran Samidurai · International Journal of Nonlinear Sciences and Numerical Simulation · 2021
Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.