Modelling neural network performance through response surface methodology for classifying wood veneer defects

Abstract

This paper describes the use of response surface methodology (RSM) to model the performance of a neural network. This is in order to help select the values for the neural network parameters. The method was applied to design a multilayer perceptron network for classifying surface defects on wood veneer. The results show that the performance of the neural network was improved by this method, but extrapolation outside the tested parameter range should be avoided.