Performance evaluation of linear regression, back-propagation neural network and linear Hebbian neural network for fitting linear functions

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various fields. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which is the most suitable one for a specific function-fitting problem. In this paper, the authors evaluate performances of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on computational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned. [In Korean.]