Stability and synchronization analysis of inertial memristive neural networks with time delays

R Rakkiyappan, S Premalatha, A Chandrasekar… - Cognitive …, 2016 - Springer
Cognitive neurodynamics, 2016Springer
This paper is concerned with the problem of stability and pinning synchronization of a class
of inertial memristive neural networks with time delay. In contrast to general inertial neural
networks, inertial memristive neural networks is applied to exhibit the synchronization and
stability behaviors due to the physical properties of memristors and the differential inclusion
theory. By choosing an appropriate variable transmission, the original system can be
transformed into first order differential equations. Then, several sufficient conditions for the …
Abstract
This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results.
Springer
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