Topics in matrix analysis
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Journal of Computational and Applied Mathematics
Mathematics and Computers in Simulation
BAM-type Cohen-Grossberg neural networks with time delays
Mathematical and Computer Modelling: An International Journal
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Delay-independent stability in bidirectional associative memory networks
IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses
Neural Processing Letters
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Abstract: In this paper, the exponential stability analysis for the bidirectional associative memory neural network model with both time-varying delays and general activation functions is considered. Neither the boundedness and the monotony on these activation functions nor the differentiability on the time-varying delays are assumed. By employing Lyapunov functional and the linear matrix inequality (LMI) approach, several new sufficient conditions in LMI form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literature, which is demonstrated via an example with simulation.