Adaptive learning networks of multi-stage fuzzy production rules in expert system of grinding characteristics

Abstract

Abstract The cross validation technique offers a criterion to measure the degree of approximation of a mathematical model. The advantage of the leaving-one-out error estimation and the k-fold cross validation is that almost all the available samples are used for training whereas all the samples are used for testing. But because these techniques are computationally expensive, it has often been reserved for problems with small sample size. This paper discusses the validity of Akaike's AIC for selecting the number of layers in an adaptive learning network of GMDH type whose partial descriptions are represented by Gaussian functions. In numerical examples, several computer simulations of learning and comparisons of AIC with cross validation procedure are shown. The expert system for identification of grinding characteristics is discussed.