An LMI-Based approach to the global stability of bidirectional associative memory neural networks with variable delay

Authors:
Minghui Jiang;Yi Shen;Xiaoxin Liao
Affiliations:
Institute of Nonlinear Complex Systems, Three Gorges University, Yichang, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 7
Cited 0

Bidirectional associative memories

IEEE Transactions on Systems, Man and Cybernetics
Global exponential stability in continuous-time and discrete-time delayed bidirectional neural networks

Physica D
Convergence Analysis of Recurrent Neural Networks (Network Theory and Applications, V. 13)

Convergence Analysis of Recurrent Neural Networks (Network Theory and Applications, V. 13)
Global asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays

Neurocomputing
Global stability for cellular neural networks with time delay

IEEE Transactions on Neural Networks
An analysis of global asymptotic stability of delayed cellular neural networks

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the linear matrix inequality (LMI), new sufficient conditions on the global exponential stability and asymptotic stability of bidirectional associative memory neural networks with variable delay are presented, and exponential converging velocity index is estimated. Furthermore, the results in this paper are less conservative than the ones reported so far in the literature. One example is given to illustrate the feasibility of our main results.