Universal approximation using radial-basis-function networks
Neural Computation
Approximation by superposition of sigmoidal and radial basis functions
Advances in Applied Mathematics
Feedforward nets for interpolation and classification
Journal of Computer and System Sciences
Neural networks for localized approximation
Mathematics of Computation
Simultaneous Lp-approximation order for neural networks
Neural Networks
Neural Networks for Approximation of Real Functions with the Gaussian Functions
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Letters: Convex incremental extreme learning machine
Neurocomputing
Constructive approximation to real function by wavelet neural networks
Neural Computing and Applications
Constructive approximate interpolation by neural networks
Journal of Computational and Applied Mathematics
Quasi-interpolation for data fitting by the radial basis functions
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
IEEE Transactions on Neural Networks
Multiscale approximation with hierarchical radial basis functions networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Accuracy analysis for wavelet approximations
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network
Expert Systems with Applications: An International Journal
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For the multidimensional continuous function, using constructive feedforward wavelet RBF neural network, we prove that a wavelet RBF neural network with n+1 hidden neurons can interpolate n+1 multidimensional samples with zero error. Then we prove they can uniformly approximate any continuous multidimensional function with arbitrary precision. This method can avoid the defects of conventional neural networks using learning algorithm in practice. The correctness and effectiveness are verified through four numeric experiments.