Performance of Models Based on a Linear Regression and Neural Networks

Abstract

In this paper the comparison of models based on a linear regression and neural networks is presented. The analyzed models are the generalized profile function models, GPFM. The GPFM provides approximations of the individual models (individual stem profile models) of the objects using only two basic measurements. The performances of the obtained GPFM, by using the linear regression relations and neural networks are compared by a test platform in MATLAB with a simple graphic user interface. It is shown that application of both linear regression and neural networks provides the efficient and robust generalized model with very good performances.