Global asymptotic stability of delayed bi-directional associative memory neural networks
Applied Mathematics and Computation
New Results on the Robust Stability of Cohen---Grossberg Neural Networks with Delays
Neural Processing Letters
Robust Stability in Cohen---Grossberg Neural Network with both Time-Varying and Distributed Delays
Neural Processing Letters
Computers & Mathematics with Applications
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Neural Networks
BAM-type Cohen-Grossberg neural networks with time delays
Mathematical and Computer Modelling: An International Journal
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper discusses global exponential stability of equilibrium point for a class of Cohen-Grossberg BAM neural networks with delays. Under the assumptions that the activation functions only satisfy global Lipschitz conditions and the behaved functions only satisfy sign conditions, by applying the linear matrix inequality (LMI) method, degree theory and some inequality technique, a novel LMI-based sufficient condition is established for global exponential stability of the concerned neural networks. In our result, the assumption on the activation functions is less conservative than the assumption for monotonicity in Nie and Cao (2009) [28] and the assumption on the behaved functions is also less conservative than the assumption for differentiability in Nie and Cao (2009) [28], Xia (2010) [30], Zhou and Wan (2009) [31] and Zhang et al. (2012) [35].