New results on exponential input-to-state stability analysis of memristor based complex-valued inertial neural networks with proportional and distributed delays

The present work accumulates the Exponential input-to-state stability (EISS) criteria of memristor based delayed complexvalued neural networks (DMCNN) associated with an inertial term and time-varying delays. Here two varieties of time-varying delays are provided, namely proportional and distributed delays. In this study, the delayed memristor neural networks (MNN) is constructed on the basis of second order complex-valued space. In addition, the sufficient conditions are proposed to ensure the EISS by using the combination of non-smooth analysis, set-valued maps, Lyapunov-Krasovskii functional having double integral terms and Kirchhoff's matrix tree theorem, moreover we employ Cauchy-Schwarz inequality & some inequality techniques. At the end of this work, the hypothesis has been established with an illustrative example along with the simulations. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.