Global Dissipative Examination of Delayed Memristive Inertial Neural Networks with Uncertain Parameters

Abstract

The present article addresses the discussion of dissipative examination of Dynamic Systems, namely Inertial Neural Networks with memristor, parameter uncertainity and delays such as time-varying, distributed. A suitable variable substitution is implemented to convert the inertial system to the first order differential system. Exploiting the concept of matrix measure and properties to the Lyapunov function, a sufficient criteria for dissipative of the dynamical system is achieved through a generalized Halanay Inequality. Concurrently, the globally attractive sets are extracted from the network system with bound. The derived results are new-fangled concerning the dynamical systems with the complex- the inertial and memristor term along with the mixed delays and parameter uncertainties. At the end, the numerical simulations are presented for the better clarification and testimonial of obtained dissipative criteria with pictorial representation.