Adaptive fuzzy modeling versus artificial neural networks

In this paper two areas of soft computing (fuzzy modeling and artificial neural networks) are discussed. Based on the fundamental mathematical similarity of fuzzy techniques and radial basis function networks a new training algorithm for fuzzy models is introduced. A feed forward neural network (NN), a radial basis function network (RBF) and a trained fuzzy algorithm are compared for regional yield estimation of agricultural crops (winter rye, winter barley). As training pattern a data set from a training region (Maerkisch-Oderland district, Germany) and as test pattern a data set from a three times larger region were used. Specific advantages and disadvantages of these methods for the estimation of yield were discussed.