Patent ID: 6119112
Filing Date: 2000-09-12
Classification: G06N

Abstract:
A computer implemented process for training neural networks to their optimal training point, the process comprising the steps of:initializing neural network node weight values;dividing a set of neural network training data into a training set and a test set, said training data having input and output values;applying said training set to said neural network and adjusting said weight values in response to an output of said applying said training set;applying said test set to said neural network with said adjusted weight values;calculating an output variance based upon said test set and an output of said applying said test set;testing said variance byi) storing a first variance as a minimum variance;ii) comparing each calculated variance with said minimum variance;iii) replacing said minimum variance with said calculated variance if said calculated variance is less than said stored minimum variance;iv) storing a variance trend wherein said variance trend comprises a user determined number of past calculated variance values; andv) signaling that said variance has reached a minimum if said variance trend is increasing from said stored minimum variance; andin response to said testing step, terminating said training if said testing indicates said variance has reached a minimum, or repeating the applying said training set, applying said test set, calculating an output variance and testing said variance steps if said variance has not reached a minimum.