Selection of the optimum number of hidden layers in neuro-fuzzy GMDH
H. Ichihashi, Naohiko Harada, K. Nagasaka · 2002
An adaptive learning network (ALN) of group method of data handling (GMDH) type with error backpropagation is proposed, in which Gaussian radial basis functions (RBF) networks are applied to the partial descriptions of the GMDH. Optimum number of hidden layers in the ALN is selected applying the differential minimum bias criterion (DMC) and the Akaike's information criterion. The validity of these two criteria are confirmed with the cross validation technique and the average mean log-likelihood. >