Input-to-state stability of discrete-time memristive neural networks with two delay components

New results on stability analysis and extended dissipative conditions for uncertain memristive neural networks with two additive time‐varying delay components and reaction‐diffusion terms

“…A new NNs model with two additive time‐varying delay components was proposed in References 33,34, and 36‐39, which has a great application background in network‐based control and remote control. Thus, it is of significance to analyze and design MNNs with two additive time‐varying delay components and many successful investigations have been developed on the topic 40‐44 . In Reference 41, Fu et al studied the problems of dissipativity and passivity analysis for MNNs with two additive time‐varying delays based on an improved Lyapunov‐Krasovskii functional (LKF) with triple integral terms, the reciprocally convex combination technique, and the Wirtinger‐based integral inequality with free‐weighting matrices technique.…”

“…In Reference 43, Chandrasekar et al investigated the problem of synchronization of MNNs with two delay components on the basis of Jensen inequality skill and a newly second‐order reciprocally convex approach. In Reference 44, by constructing several novel LKFs, free‐weighting matrices and using some techniques such as refined Jensen‐based inequalities, mathematical induction, dynamic delay interval method, Fu et al discussed the input‐to‐state stability problem of discrete‐time MNNs with two delay components. It is worth noting that all the mentioned results ignore the effect of the reaction‐diffusion phenomenon, so the subject on extended dissipative analysis of MNNs with both two additive time‐varying delay components and reaction‐diffusion terms has not been reported until now.…”