LEARNING ON A GENERAL NETWORK Amir
نویسنده
چکیده
LEARNING ON A GENERAL NETWORK Amir F. Atiya Department of Electrical Engineering California Institute of Technology Ca 91125 This paper generalizes the backpropagation method to a general network containing feedback t;onnections. The network model considered consists of interconnected groups of neurons, where each group could be fully interconnected (it could have feedback connections, with possibly asymmetric weights), but no loops between the groups are allowed. A stochastic descent algorithm is applied, under a certain inequality constraint on each intra-group weight matrix which ensures for the network to possess a unique equilibrium state for every input.
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