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Differential equations accompanying neural networks and solvable nonlinear learning machines

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1 Author(s)
S. Watanabe ; Res. & Dev. Center, Ricoh Co. Ltd., Yokohama, Japan

Solvable models of nonlinear learning machines are analyzed based on the theory of ordinary differential equations. It is shown that a function approximation neural network automatically extracts an accompanying differential equation from learning samples and that optimal parameters can be found without recursion procedures.

Published in:

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on  (Volume:3 )

Date of Conference:

25-29 Oct. 1993