Learning dynamics in second order networks |
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| Institution: | 1. School of Electrical Engineering, Tel Aviv University, Ramat Aviv, Israel;2. School of Mathematics and Big Data, Foshan University, Foshan, China;3. Key Laboratory of System and Control, Academy of Mathematics and Systems Science, Academia Sinica, Beijing 100190, China |
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| Abstract: | A model of a second order neural network incorporating a weight adjusting learning component and time delays in processing and transmission is formulated. Delay-independent sufficient conditions are derived for the existence of an asymptotically and exponentially stable equilibrium state. The learning dynamics is modelled with an unsupervised Hebbian-type learning algorithm together with a forgetting term. If there is nothing for the network to learn or if there are no second order synaptic interactions, then our analysis will correspond to one of the standard model of neural networks. |
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