Developments to the Back - Propagation Learning Algorithm
نویسندگان
چکیده
The original back-propagation methods were plagued with variable parameters which affected both the convergence properties of the training and the generalisation abilities of the resulting network. These parameters presented many difficulties when attempting to use these networks to solve particular mapping problems. A combination of established numerical minimisation methods (Polak-Ribiere Conjugate gradient descent) with a novel idea for stepping out of local minima has produced a network minimisation package which is self optimising and has essentially no free parameters.
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