Approximation by superposition of sigmoidal and radial basis functions
Advances in Applied Mathematics
Radial basis functions for the multivariate interpolation of large scattered data sets
Journal of Computational and Applied Mathematics - Special issue: Proceedings of the 9th International Congress on computational and applied mathematics
Shape-Preserving MQ-B-Splines Quasi-Interpolation
GMP '04 Proceedings of the Geometric Modeling and Processing 2004
Local hybrid approximation for scattered data fitting with bivariate splines
Computer Aided Geometric Design
Constructive approximate interpolation by neural networks
Journal of Computational and Applied Mathematics
Constructive approximation to multivariate function by decay RBF neural network
IEEE Transactions on Neural Networks
The multidimensional function approximation based on constructive wavelet RBF neural network
Applied Soft Computing
Quasi-interpolation for surface reconstruction from scattered data with radial basis function
Computer Aided Geometric Design
Multi-level hermite variational interpolation and quasi-interpolation
The Visual Computer: International Journal of Computer Graphics
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Quasi-interpolation by the radial basis functions is discussed in this paper. We construct the approximate interpolant with Gaussion function. The suitable value of the shape parameter is suggested. The given approximate interpolants can approximately interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. They can approximate the corresponding exact interpolants with the same radial basis functions. The given method is simple without solving a linear system. Numerical examples show that the given method is effective.