Asymptotic Behavior of a Neural Network with Dynamic External Input

Conférence Chapitre d'actes Asymptotic Behavior of a Neural Network with Dynamic External Input eng 1998 author Labbi Abderrahim 113432 author Rida Ahmed 98330 129-134 10.1007/978-1-4471-1599-1_15 978-3-540-76263-8 http://link.springer.com/10.1007/978-1-4471-1599-1_15 In this paper we show how a recurrent neural network, of shunting type, receiving changing input can be used for pattern classification or association. An important feature of the proposed network is its ability to continuously process environmental (external) input. Such ability is very useful if one is to design a real-time reactive system in an unexpectedly changing environment. We firstly state sufficient conditions under which the network dynamics is convergent to stable punctual attractors. Therefore, we show that relaxing the condition on symmetry of connectivity and making a local quadratic approximation of the dynamics can lead the network into an oscillatory mode. The corresponding limit cycles are shown to be stable. Application of the network with punctual attractor dynamics to navigation in a changing environment is pointed out but is not in the scope of this paper. Dewey 025.063 London Springer 13 Centre universitaire d'informatique 0D3 32 Département d'informatique 18 false 895 Computer Vision and Multimedia Laboratory Proceedings of the 8th International Conference on Artificial Neural Networks, ICANN 98 2–4 September 1998 Skövde (Sweden) Centres et instituts / Centre universitaire d'informatique Faculté des sciences / Département d'informatique

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Asymptotic Behavior of a Neural Network with Dynamic External Input	Proceedings of the 8th International Conference on Artificial Neural Networks, ICANN 98			1998	309	0

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Rida, Ahmed