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Approximate Interpolation by Neural Networks with the Inverse Multiquadric Functions

2007
For approximate interpolation, a type of single-hidden layer feedforward neural networks with the inverse multiquadric activation function is presented in this paper. We give a new and quantitative proof of the fact that a single layer neural networks with n+1 hidden neurons can learn n + 1 distinct samples with zero error.
openaire   +1 more source

A comparison of efficiency and error convergence of multiquadric collocation method and finite element method

Engineering Analysis With Boundary Elements, 2003
Jichun Li, Alexander H -D Cheng
exaly  

Modified multiquadric methods for scattered data interpolation over a sphere

Computer Aided Geometric Design, 1990
Helmut Pottmann
exaly  

Multivariate quasi-interpolation schemes for dimension-splitting multiquadric

Applied Mathematics and Computation, 2005
Leevan Ling
exaly  

Analysis of anisotropic plate using multiquadric radial basis function

Engineering Analysis With Boundary Elements, 2007
R K Misra
exaly  

Fast Evaluation of Multiquadric RBF Sums by a Cartesian Treecode

SIAM Journal of Scientific Computing, 2011
Robert Krasny
exaly  

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