Complete Convergence of Competitive Neural Networks with Different Time Scales
Neural Processing Letters
Passivity Analysis of Dynamic Neural Networks with Different Time-scales
Neural Processing Letters
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Neural Networks
Intra-pulse modulation recognition of unknown radar emitter signals using support vector clustering
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Passivity analysis for neuro identifier with different time-scales
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Dynamics of competitive neural networks with inverse lipschitz neuron activations
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of such a neural network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. We present a new method of analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We are able to show the conditions under which the solutions of such a system are bounded being less restrictive than with the K-monotone theory, singular perturbation theory, or those based on supervised synaptic learning. We prove the existence and the uniqueness of the equilibrium. A strict Lyapunov function for the flow of a competitive neural system with different time scales is given and based on it we are able to prove the global exponential stability of the equilibrium point.