Letters: Triple Lyapunov functional technique on delay-dependent stability for discrete-time dynamical networks

Authors:
Ting Wang;Mingxiang Xue;Shumin Fei;Tao Li
Affiliations:
-;-;-;-
Venue:
Neurocomputing
Year:
2013

Citing 20
Cited 0

Exponential stability of continuous-time and discrete-time cellular neural networks with delays

Applied Mathematics and Computation
Exponential stability preservation in discrete-time analogues of artificial neural networks with distributed delays

Journal of Computational and Applied Mathematics
Asymptotic stability for neural networks with mixed time-delays: The discrete-time case

Neural Networks
Technical communique: Improved delay-range-dependent stability criteria for linear systems with time-varying delays

Automatica (Journal of IFAC)
Synchronization stability of continuous/discrete complex dynamical networks with interval time-varying delays

Neurocomputing
Technical communique: A delay-partitioning approach to the stability analysis of discrete-time systems

Automatica (Journal of IFAC)
Improved delay-dependent stability condition of discrete recurrent neural networks with time-varying delays

IEEE Transactions on Neural Networks
Stability analysis for stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays

Neurocomputing
LMI approach for stationary oscillation of interval neural networks with discrete and distributed time-varying delays under impulsive perturbations

IEEE Transactions on Neural Networks
Technical communique: Reciprocally convex approach to stability of systems with time-varying delays

Automatica (Journal of IFAC)
Impulsive hybrid discrete-time Hopfield neural networks with delays and multistability analysis

Neural Networks
Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: An LMI approach

Journal of Computational and Applied Mathematics
Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

Neurocomputing
Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays

Expert Systems with Applications: An International Journal
Neural networks letter: Robust stability analysis of a class of neural networks with discrete time delays

Neural Networks
Exponential Stability of Stochastic Neural Networks With Both Markovian Jump Parameters and Mixed Time Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay

IEEE Transactions on Neural Networks
Stability analysis for a class of neutral-type neural networks with Markovian jumping parameters and mode-dependent mixed delays

Neurocomputing
LMI-Based Approach for Global Asymptotic Stability Analysis of Recurrent Neural Networks with Various Delays and Structures

IEEE Transactions on Neural Networks
A new robust stability criterion for dynamical neural networks with multiple time delays

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this letter, through constructing some novel triple Lyapunov-Krasovskii functional (LKF) terms, two novel sufficient conditions are established to guarantee a class of discrete-time delayed dynamical networks to be asymptotically stable, in which the information of time-delay can be fully utilized. Through employing the reciprocal convex technique, some previously ignored terms can be reconsidered when estimating the time difference of LKF and the criteria can be presented via linear matrix inequalities (LMIs), whose solvability heavily depends on the information of addressed systems. Finally, two numerical examples will be provided to show that the achieved conditions can be less conservative than some existing results.