Dynamical Behaviors of a Large Class of General Delayed Neural Networks
Neural Computation
Journal of Computational and Applied Mathematics
Existence and learning of oscillations in recurrent neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In the paper, we present the existence of mn locally exponentially stable equilibrium points for a general n-dimensional delayed neural networks with multilevel activation functions which have m segments Furthermore, the theory is also extended to the existence of mn locally exponentially stable limit cycles for the n-dimensional delayed neural networks evoked by periodic external input The results are obtained through formulating parameter conditions, which are easily verifiable and independent of the delay parameter Two numerical examples are given to show the effectiveness of the theory.