New results on robust exponential stability for discrete recurrent neural networks with time-varying delays

Authors:
Zhengguang Wu;Hongye Su;Jian Chu;Wuneng Zhou
Affiliations:
National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou 310027, PR China;National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou 310027, PR China;National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou 310027, PR China;College of Information Science and Technology, Donghua University, Shanghai 200051, PR China
Venue:
Neurocomputing
Year:
2009

Citing 18
Cited 8

A stabilization algorithm for a class of uncertain linear systems

Systems & Control Letters
Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays

Neural Processing Letters
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays

Neural Networks
A new approach to exponential stability analysis of neural networks with time-varying delays

Neural Networks
Robust stability of discrete-time stochastic neural networks with time-varying delays

Neurocomputing
Delay-dependent robust stability criteria for uncertain systems with interval time-varying delay

Journal of Computational and Applied Mathematics
Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach

Neurocomputing
Asymptotic stability analysis of neural networks with successive time delay components

Neurocomputing
Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays

Neurocomputing
Letters: State estimation for discrete-time neural networks with time-varying delays

Neurocomputing
Passivity analysis of discrete-time stochastic neural networks with time-varying delays

Neurocomputing
New results on passivity analysis of uncertain neural networks with time-varying delays

International Journal of Computer Mathematics
Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
State estimation for delayed neural networks

IEEE Transactions on Neural Networks
An improved global asymptotic stability criterion for delayed cellular neural networks

IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks

IEEE Transactions on Neural Networks
Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays

IEEE Transactions on Neural Networks

Robust exponential stability of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays

IEEE Transactions on Neural Networks
Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays

Neurocomputing
Synchronization for an array of coupled stochastic discrete-time neural networks with mixed delays

Neurocomputing
Invariant set and attractor of discrete-time impulsive recurrent neural networks

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Robust stability for uncertain stochastic neural network with delay and impulses

Neurocomputing
On synchronization criterion for coupled discrete-time neural networks with interval time-varying delays

Neurocomputing
A new robust stability criterion for dynamical neural networks with multiple time delays

Neurocomputing
New delay-dependent exponential stability for discrete-time recurrent neural networks with mixed time-delays

International Journal of Innovative Computing and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper is concerned with the problem of robust exponential stability analysis for uncertain discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, some novel stability conditions are proposed via a new Lyapunov function. Neither any model transformation nor free-weighting matrices are employed in our theoretical derivation. The established stability criteria significantly improve and simplify some existing stability conditions. Numerical examples are given to demonstrate the effectiveness of the proposed methods.