Published April 13, 2009 | Version v1
Journal article

Improved result on stability analysis of discrete stochastic neural networks with time delay

  • 1. National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou 310027 (China)
  • 2. College of Information Science and Technology, Donghua University, Shanghai 200051 (China)

Description

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.physleta.2009.02.056

Additional details

Identifiers

DOI
10.1016/j.physleta.2009.02.056;
PII
S0375-9601(09)00243-6;

Publishing Information

Journal Title
Physics Letters. A
Journal Volume
373
Journal Issue
17
Journal Page Range
p. 1546-1552
ISSN
0375-9601
CODEN
PYLAAG

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
41059491
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
LYAPUNOV METHOD; MATRICES; NEURAL NETWORKS; STABILITY; STOCHASTIC PROCESSES; TIME DELAY
Descriptors DEC
CALCULATION METHODS

Optional Information

Copyright
Copyright (c) 2009 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.