Non-reduced order strategies for global dissipativity of memristive neutral-type inertial neural networks with mixed time-varying delays

The issue of the global dissipativity of memristive neutral-type inertial neural networks with distributed and discrete time-varying delays is discussed without converting the original system to first-order equations. By taking some new Lyapunov-Krasovskii functionals and adopting inequality techniques, several effective criteria formulated by testable algebraic inequalities are derived to assure the global dissipativity and exponential dissipativity for the concerned models, which generalize and refine some previous results. Different from existing ones, the proposed Lyapunov-Krasovskii functionals contain not only the state variables but also their derivatives. The estimations of the globally attractive sets and globally exponentially attractive sets are also proposed. Two examples are given to validate the efficiency of the theoretical results. (c) 2021 Elsevier B.V. All rights reserved.