Stochastic robust finite-time boundedness for semi-Markov jump uncertain neutral-type neural networks with mixed time-varying delays via a generalized reciprocally convex combination inequality

摘要

This article investigates the stochastic robust finite-time boundedness problem for semi-Markov jump uncertain (SMJU) neutral-type neural networks with distributed and additive time-varying delays (TDs). To derive less conservative stability criteria, a generalized reciprocally convex combination inequality (RCCI) is first proposed, which includes the existing RCCIs as its special cases. By taking full advantage of the characteristics of various TDs and SMJU parameters, a novel suitable Lyapunov-Krasovskii functional is provided. Then, with the virtue of the new RCCI and other analysis approaches, some new criteria guaranteeing the underlying systems are stochastically robustly finite-time bounded or stable and are derived in the form of linear matrix inequalities. Finally, three numerical examples are given to show the validity of the approaches presented in this article.