Letters: Robust stability for neural networks with time-varying delays and linear fractional uncertainties

Authors:
Tao Li;Lei Guo;Changyin Sun
Affiliations:
The Research Institute of Automation, Southeast University, Nanjing 210096,China;The School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100083, China;The Research Institute of Automation, Southeast University, Nanjing 210096,China
Venue:
Neurocomputing
Year:
2007

Citing 6
Cited 12

Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach

Neural Networks
An analysis of exponential stability of delayed neural networks with time varying delays

Neural Networks
Global robust stability of interval neural networks with multiple time-varying delays

Mathematics and Computers in Simulation
Improved global robust asymptotic stability criteria for delayed cellular neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

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

IEEE Transactions on Neural Networks

Letters: Adaptive RBF neural-networks control for a class of time-delay nonlinear systems

Neurocomputing
Stability Criteria with Less Variables for Neural Networks with Time-Varying Delay

ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Novel delay-dependent stability criteria of neural networks with time-varying delay

Neurocomputing
Novel delay-dependent criteria for global robust exponential stability of delayed cellular neural networks with norm-bounded uncertainties

Neurocomputing
Novel delay-dependent asymptotic stability criteria for neural networks with time-varying delays

Journal of Computational and Applied Mathematics
Delay-dependent robust stability for Hopfield neural networks of neutral-type

Neurocomputing
Delay-interval dependent robust stability criteria for stochastic neural networks with linear fractional uncertainties

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

Neurocomputing
Stability analysis for neural networks with time-varying delay: A more general delay decomposition approach

Neurocomputing
Delay-dependent passivity criterion for discrete-time delayed standard neural network model

Neurocomputing
Improved stability criteria for uncertain delayed neural networks

Neurocomputing
New passivity conditions with fewer slack variables for uncertain neural networks with mixed delays

Neurocomputing

Quantified Score

Hi-index	0.02

Visualization

Abstract

The problem of robust stability for neural networks with time-varying delays and parameter uncertainties is investigated in this paper. The parameter uncertainties are described to be of linear fractional form, which include the norm bounded uncertainties as a special case. By introducing a new Lyapunov-Krasovskii functional and considering the additional useful terms when estimating the upper bound of the derivative of Lyapunov functional, new delay-dependent stability criteria are established in term of linear matrix inequality (LMI). It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.