USING TAGUCHI METHOD OF EXPERIMENTAL-DESIGN TO CONTROL ERRORS IN LAYERED PERCEPTRONS

A significant problem in the design and construction of an artificial neural network for function approximation is limiting the magnitude an d the variance of errors when the network is used in the field, Networ k errors can occur when the training data does not faithfully represen t the required function due to noise or low sampling rates, when the n etwork's flexibility does not match the variability of the data, or wh en the input data to the resultant network is noisy, This paper report s on several experiments whose purpose was to rank the relative signif icance of these error sources and thereby find neural network design p rinciples or limiting the magnitude and variance of network errors.