Multilayer feedforward networks are universal approximators
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Median radial basis function neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
POFGEC: growing neural network of classifying potential function generators
International Journal of Knowledge Engineering and Soft Data Paradigms
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In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our learning algorithm are introduced. We investigate the change of cluster shape with the dimension of the input data, the choice of univariate potential function, and the construction of multivariate potential functions. Several data sets are considered to demonstrate the classification performance on the training and testing exemplars as well as compare DYPOF with other neural networks.