Passivity of memristor-based BAM neural networks with different memductance and uncertain delays

被引：44

作者：

Anbuvithya, R. ^{[1
]}

Mathiyalagan, K. ^{[2
]}

Sakthivel, R. ^{[3
,4
]}

Prakash, P. ^{[5
]}

机构：

[1] Natl Inst Technol, Dept Math, Tiruchirappalli 620015, Tamil Nadu, India

[2] Anna Univ, Dept Math, Reg Ctr, Coimbatore 641047, Tamil Nadu, India

[3] Sri Ramakrishna Inst Technol, Dept Math, Coimbatore 641010, Tamil Nadu, India

[4] Sungkyunkwan Univ, Dept Math, Suwon 440746, South Korea

[5] Periyar Univ, Dept Math, Salem 636011, India

来源：

COGNITIVE NEURODYNAMICS | 2016年 / 10卷 / 04期

关键词：

Memristor; BAM neural networks; Passivity; Linear matrix inequality; Uncertain delay; TIME-VARYING DELAY; H-INFINITY CONTROL; EXPONENTIAL PASSIVITY; ROBUST STABILITY; SYNCHRONIZATION; STABILIZATION; DISCRETE; CRITERIA; SYSTEMS;

D O I：

10.1007/s11571-016-9385-1

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

引用

页码：339 / 351

页数：13

共 45 条

[1] New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays [J].

Ali, M. Syed ;

Saravanakumar, R. ;

Cao, Jinde .

NEUROCOMPUTING, 2016, 171 :1533-1547

[2]

Anderson B. D. O., 1973, Network Analysis and Synthesis: A Modern Systems Approach

[3]

[Anonymous], 1988, Differential Equations with Discontinuous Righthand Sides

[4] Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays [J].

Cai, Zuowei ;

Huang, Lihong .

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (05) :1279-1300

[5] Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors [J].

Cantley, Kurtis D. ;

Subramaniam, Anand ;

Stiegler, Harvey J. ;

Chapman, Richard A. ;

Vogel, Eric M. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (04) :565-573

[6] Some Integrals Involving q-Laguerre Polynomials and Applications [J].

Cao, Jian .

ABSTRACT AND APPLIED ANALYSIS, 2013,

[7] Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays [J].

Cao, Jinde ;

Wan, Ying .

NEURAL NETWORKS, 2014, 53 :165-172

[8] Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach [J].

Chandrasekar, A. ;

Rakkiyappan, R. ;

Cao, Jinde ;

Lakshmanan, S. .

NEURAL NETWORKS, 2014, 57 :79-93

[9] MEMRISTOR - MISSING CIRCUIT ELEMENT [J].

CHUA, LO .

IEEE TRANSACTIONS ON CIRCUIT THEORY, 1971, CT18 (05) :507-+

[10] Exponential passivity of BAM neural networks with time-varying delays [J].

Du, Yuanhua ;

Zhong, Shouming ;

Zhou, Nan ;

Nie, Lei ;

Wang, Wenqin .

APPLIED MATHEMATICS AND COMPUTATION, 2013, 221 :727-740

← 1 2 3 4 5 →