Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays

In this paper, the problem of passivity analysis is investigated for uncertain stochastic fuzzy interval neural networks with time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov–Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved by some standard numerical packages. Finally, numerical examples are given to show the effectiveness and merits of the proposed method.

► Delay-dependent robust passivity analysis for stochastic fuzzy interval neural networks has been discussed. ► The restrictions on the boundedness, monotony and differentiability of the activation functions have been removed. ► Free-weighting matrices have been introduced to reduce conservatism. ► The proposed criteria are given in terms of LMIs which can be easily solved through standard numerical packages. ► The superiority of the proposed results are shown by means of a numerical example.