Comparison of generalized profile function models based on linear regression and neural networks

Abstract

In this paper, the generalized profile function models, GPFMs, based on linear regression and neural networks, are compared. GPFM provides an approximation of individual models (models of individual stem profile) facility using only two basic measurements. GPFM based on neural network is obtained as the average of all available normalized individual models. It is shown that the application of neural networks provides a generalized model with good performance.