New LMI-Based Passivity Criteria for Neutral-Type BAM Neural Networks with Randomly Occurring Uncertainties

In this paper, we study the passivity analysis for a class of neutral-type BAM neural networks with time-varying delays and randomly occurring uncertainties as well as generalized activation functions. Linear matrix inequality (LMI) approach together with the construction of proper Lyapunov–Krasovskii functional involving triple integrals and augmented type constraint is implemented to derive a new set of sufficient conditions for obtaining the required result. More precisely, first we derive the passivity condition for BAM neural networks without uncertainties and then the result is extended to the case with randomly occurring uncertainties. In particular, the presented results depend not only upon discrete delay but also distributed time varying delay. The obtained passivity conditions are formulated in terms of linear matrix inequalities that can be easily solved by using the MATLAB-LMI toolbox. Finally, the effectiveness of the proposed passivity criterion is demonstrated through numerical example.