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In the following report the problem of selecting proper training sets for neural network time series prediction or function approximation is addressed. As a result of analyzing the relation between approximation and generalization, a new measure, the generalization factor is introduced. Using this factor and cross validation a new algorithm, the dynamic(More)
Time series prediction is one of the major applications of neural networks. After a short introduction into the basic theoretical foundations we argue that the iterated prediction of a dynamical system may be interpreted as a model of the system dynamics. By means of RBF neural networks we describe a modeling approach and extend it to be able to model(More)
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