Robust stability of interval bidirectional associative memory neural network with time delays

Authors:
Xiaofeng Liao;Kwok-wo Wong
Affiliations:
Dept. of Comput. Sci. & Eng., Chongqing Univ., China;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2004

Citing 0
Cited 13

Robust Control of Uncertain Stochastic Recurrent Neural Networks with Time-varying Delay

Neural Processing Letters
Robust global periodicity of interval bidirectional associative memory networks with finite distributed delays

International Journal of Systems Science
Stability of non-autonomous bidirectional associative memory neural networks with delay

Neurocomputing
New stability criteria for BAM neural networks with time-varying delays

Neurocomputing
Direct adaptive fuzzy control for nonlinear systems with time-varying delays

Information Sciences: an International Journal
Neural networks letter: Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters

Neural Networks
New results for global robust asymptotic stability of BAM neural networks with time-varying delays

Neurocomputing
New stability criteria for recurrent neural networks with a time-varying delay

International Journal of Automation and Computing
Impulsive effects on stability of high-order BAM neural networks with time delays

Neurocomputing
Robust stability of interval delayed neural networks

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Stochastic robust stability analysis for markovian jump discrete-time delayed neural networks with multiplicative nonlinear perturbations

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Neural networks letter: Robust stability analysis of a class of neural networks with discrete time delays

Neural Networks
A new condition for robust stability of uncertain neural networks with time delays

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.