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

被引：25
作者：
Iswarya, M. [1 ]
Raja, R. [2 ]
Cao, C. [3 ,4 ,7 ]
Niezabitowski, M. [5 ]
Alzabut, J. [6 ]
Maharajan, C.
机构：
[1] Alagappa Univ, Dept Math, Karaikkudi 630 003, India
[2] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630 003, India
[3] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China
[4] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea
[5] Silesian Tech Univ, Akademicka 16, PL-44100 Gliwice, Poland
[6] Prince Sultan Univ, Dept Math & Gen Sci, Riyadh 12435, Saudi Arabia
[7] VSB Engn Coll, Dept Math, Karur, India
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2022年 / 201卷
关键词：
Complex-valued neural networks; EISS; Kirchhoff's matrix tree theorem; Proportional delays & Distributed time-varying delays; ROBUST STABILITY; SYNCHRONIZATION; DYNAMICS;
D O I：
10.1016/j.matcom.2021.01.020
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：440 / 461
页数：22
相关论文
共 46 条
[1]  
Aizenberg I, 2011, STUD COMPUT INTELL, V353, P1, DOI 10.1007/978-3-642-20353-4
[2]   Dynamics and oscillations of generalized high-order Hopfield neural networks with mixed delays [J].
Alimi, Adel M. ;
Aouiti, Chaouki ;
Cherif, Farouk ;
Dridi, Farah ;
M'hamdi, Mohammed Salah .
NEUROCOMPUTING, 2018, 321 :274-295
[3]   Finite time boundedness of neutral high-order Hopfield neural networks with time delay in the leakage term and mixed time delays [J].
Aouiti, Chaouki ;
Coirault, Patrick ;
Miaadi, Foued ;
Moulay, Emmanuel .
NEUROCOMPUTING, 2017, 260 :378-392
[4]   Neutral impulsive shunting inhibitory cellular neural networks with time-varying coefficients and leakage delays [J].
Aouiti, Chaouki .
COGNITIVE NEURODYNAMICS, 2016, 10 (06) :573-591
[5]   STABILITY AND DYNAMICS OF SIMPLE ELECTRONIC NEURAL NETWORKS WITH ADDED INERTIA [J].
BABCOCK, KL ;
WESTERVELT, RM .
PHYSICA D, 1986, 23 (1-3) :464-469
[6]   DYNAMICS OF SIMPLE ELECTRONIC NEURAL NETWORKS [J].
BABCOCK, KL ;
WESTERVELT, RM .
PHYSICA D, 1987, 28 (03) :305-316
[7]   Impulsive effects on competitive neural networks with mixed delays: Existence and exponential stability analysis [J].
Balasundaram, K. ;
Raja, R. ;
Pratap, A. ;
Chandrasekaran, S. .
MATHEMATICS AND COMPUTERS IN SIMULATION, 2019, 155 :290-302
[8]   Global robust stability of delayed recurrent neural networks [J].
Cao, JD ;
Huang, DS ;
Qu, YZ .
CHAOS SOLITONS & FRACTALS, 2005, 23 (01) :221-229
[9]   Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays [J].
Cao, Yang ;
Sriraman, R. ;
Shyamsundarraj, N. ;
Samidurai, R. .
MATHEMATICS AND COMPUTERS IN SIMULATION, 2020, 171 (171) :207-220
[10]   A Delay-Dividing Approach to Robust Stability of Uncertain Stochastic Complex-Valued Hopfield Delayed Neural Networks [J].
Chanthorn, Pharunyou ;
Rajchakit, Grienggrai ;
Humphries, Usa ;
Kaewmesri, Pramet ;
Sriraman, Ramalingam ;
Lim, Chee Peng .
SYMMETRY-BASEL, 2020, 12 (05)
12345