Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays
Neural Processing Letters
Exponential periodicity and stability of delayed neural networks
Mathematics and Computers in Simulation
Learning of spatio-temporal codes in a coupled oscillator system
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Periodic oscillation and exponential stability of a class of competitive neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Complex dynamics in a simple hopfield-type neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Multistability of delayed neural networks with discontinuous activations
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Optimal design of neuro-mechanical oscillators
Computers and Structures
Dynamics of an adaptive higher-order Cohen-Grossberg model
Neurocomputing
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We study a particular class of n-node recurrent neural networks (RNNs). In the 3-node case we use monotone dynamical systems theory to show, for a well-defined set of parameters, that, generically, every orbit of the RNN is asymptotic to a periodic orbit. We then investigate whether RNNs of this class can adapt their internal parameters so as to “learn” and then replicate autonomously (in feedback) certain external periodic signals. Our learning algorithm is similar to the identification algorithms in adaptive control theory. The main feature of the algorithm is that global exponential convergence of parameters is guaranteed. We also obtain partial convergence results in the n-node case