Relative performance of the statistical learning network: An application of the price-quality relationship in the automobile

Abstract

The design and topology of a neural network is still an important and difficult task. To solve the problems of topology posed by the introduction of connexionism, new approaches are proposed, and especially a combination of induction rules with a statistical estimation of the neuron coefficients for each layer. This research aims to compare an algorithm of this SLN approach with traditional methods (regression and classical BP neural networks) using the gradient method. Methods are put into application to determine the price-quality relationship of a complex product, the automobile, according to the hedonic price model. This application of the price-quality relationship to the English automobile market leads to the conclusion that the claimed superiority of this approach is unsubstantiated since, compared to the BP neural networks and even linear regression, the performance of the GMDH method is inferior.