Delay-dependent robust stability analysis of uncertain stochastic neural networks with discrete interval and distributed time-varying delays

Authors:
P. Balasubramaniam;R. Rakkiyappan
Affiliations:
Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamilnadu, India;Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamilnadu, India
Venue:
Neurocomputing
Year:
2009

Citing 8
Cited 4

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing
Delay-interval-dependent stability of recurrent neural networks with time-varying delay

Neurocomputing
Global stability for cellular neural networks with time delay

IEEE Transactions on Neural Networks
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks
Improved conditions for global exponential stability of recurrent neural networks with time-varying delays

IEEE Transactions on Neural Networks
New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay

IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay

IEEE Transactions on Neural Networks

Stability of impulsive Hopfield neural networks with Markovian switching and time-varying delays

International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
Existence and global asymptotic stability of periodic solution for discrete and distributed time-varying delayed neural networks with discontinuous activations

Neurocomputing
Improved exponential stability criteria for neural networks with time-varying delays

Neurocomputing
New delay-dependent stability criteria for uncertain stochastic neural networks with discrete interval and distributed delays

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper is concerned with stability analysis problem for uncertain stochastic neural networks with discrete interval and distributed time-varying delays. The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov-Krasovskii functional and stochastic stability theory, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities. Some numerical examples and comparisons are provided to show that the proposed results significantly improve the allowable upper and lower bounds of delays over some existing results in the literature. Furthermore, the supplementary requirement that the time derivative of discrete time-varying delays must be smaller than the value one is not necessary to derive the results in this paper.