Neural Comput - Natural gradient learning algorithms for RBF networks.

Tópicos

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Resumo

Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation.

Resumo Limpo

radial basi function rbf network one wide use model function approxim classif mani strang behavior learn process rbf network slow learn speed exist plateaus natur gradient learn method can overcom disadvantag effect can acceler dynam learn avoid plateaus letter assum probabl densiti function pdf input activ function gaussian first introduc natur gradient learn rbf network give explicit form fisher inform matrix invers second sinc difficult calcul fisher inform matrix invers number hidden unit dimens input larg introduc adapt method natur gradient learn algorithm final give explicit form adapt natur gradient learn algorithm compar convent gradient descent method simul show propos adapt natur gradient method can avoid plateaus effect good perform rbf network use nonlinear function approxim

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