2012
DOI: 10.5402/2012/324194
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Using Radial Basis Function Networks for Function Approximation and Classification

Abstract: The radial basis function RBF network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron MLP , since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approximation capability, radial basis funct… Show more

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Cited by 154 publications
(84 citation statements)
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“…No que diz respeito às técnicas, várias são as abordagens de cálculo numérico e métodos estatísticos utilizados para a resolução desse problema, tais como: interpolação, splines, séries de potências, regressão por funções de núcleo, entre outros. Há ainda abordagens baseadas em Inteligência Computacional, como Multilayer Perceptron (MLP) [10], Radial Basis Function Network (RBFN) [11], Neuro-fuzzy Systems [12].…”
Section: Aproximação De Funçãounclassified
“…No que diz respeito às técnicas, várias são as abordagens de cálculo numérico e métodos estatísticos utilizados para a resolução desse problema, tais como: interpolação, splines, séries de potências, regressão por funções de núcleo, entre outros. Há ainda abordagens baseadas em Inteligência Computacional, como Multilayer Perceptron (MLP) [10], Radial Basis Function Network (RBFN) [11], Neuro-fuzzy Systems [12].…”
Section: Aproximação De Funçãounclassified
“…Gaussian RBFs are local and are the most commonly used across several applications. Other type of kernels for RBF can also be used with more specific purposes (Wu et al, 2012).…”
Section: Dimensionality Reduction Via Proper Orthogonal Decompositionmentioning
confidence: 99%
“…Choosing all points as RBF centers will therefore lead to an unnecessarily large network involving long training and computation times. An effective way of reducing the number of nodes in the hidden layer is by clustering the input points such that each point falls into one of the hyperspheres which collectively span the entire input space (Wu et al, 2012). Each of the RBF centers are then to be located at the centroid of each cluster.…”
Section: Dimensionality Reduction Via Proper Orthogonal Decompositionmentioning
confidence: 99%
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