A fast learning algorithm for time-delay neural networks
Information Sciences—Applications: An International Journal
Polychronization: Computation with Spikes
Neural Computation
Multiple positive solutions for a class of integral inclusions
Journal of Computational and Applied Mathematics
Computers & Mathematics with Applications
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Robust state estimation for neural networks with discontinuous activations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Stability analysis for neural dynamics with time-varying delays
IEEE Transactions on Neural Networks
Existence, learning, and replication of periodic motions in recurrent neural networks
IEEE Transactions on Neural Networks
Existence and learning of oscillations in recurrent neural networks
IEEE Transactions on Neural Networks
Weight adaptation and oscillatory correlation for image segmentation
IEEE Transactions on Neural Networks
Oscillatory neural networks for robotic yo-yo control
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Emergent synchrony in locally coupled neural oscillators
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results.