Passivity analysis of stochastic neural networks with leakage delay and Markovian jumping parameters

被引:19
作者
Ali, M. Syed [1 ]
Arik, Sabri [2 ]
Rani, M. Esther [1 ]
机构
[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India
[2] Istanbul Univ, Dept Comp Engn, TR-34320 Istanbul, Turkey
关键词
Delayed neural networks; Leakage term; Lyapunov functional; Markovian jumping parameters; Passivity; Stochastic disturbance; Time-varying delays; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; DEPENDENT STABILITY; DISTRIBUTED DELAYS; NEUTRAL-TYPE; BAM; CRITERIA;
D O I
10.1016/j.neucom.2016.08.062
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The problem of passivity analysis of stochastic neural networks with leakage delay and Markovian jumping parameters is considered in this article. By utilizing the Lyapunov functional method, the Ito differential rule and matrix analysis techniques, we establish sufficient criterion such that the stochastic neural networks is passive in the sense of expectation. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:139 / 145
页数:7
相关论文
共 43 条
[1]   Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays [J].
Ali, M. Syed .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2014, 5 (01) :13-22
[2]  
[Anonymous], 1994, LINEAR MATRIX INEQUA
[3]  
[Anonymous], 2001, Neural Networks: A Comprehensive Foundation
[4]   An improved global stability result for delayed cellular neural networks [J].
Arik, S .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 2002, 49 (08) :1211-1214
[5]   Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays [J].
Balasubramaniam, P. ;
Nagamani, G. .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (01) :732-742
[6]   Stochastic global exponential stability for neutral-type impulsive neural networks with mixed time-delays and Markovian jumping parameters [J].
Bao, Haibo ;
Cao, Jinde .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2011, 16 (09) :3786-3791
[7]  
Bevelevich V., 1968, Classical Network Synthesis
[8]   Global exponential stability and periodicity of recurrent neural networks with time delays [J].
Cao, JD ;
Wang, J .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2005, 52 (05) :920-931
[9]   On passivity analysis for stochastic neural networks with interval time-varying delay [J].
Fu, Jie ;
Zhang, Huaguang ;
Ma, Tiedong ;
Zhang, Qingling .
NEUROCOMPUTING, 2010, 73 (4-6) :795-801
[10]   Leakage delays in BAM [J].
Gopalsamy, K. .
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2007, 325 (02) :1117-1132