Name
Abstract | ||
|---|---|---|
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. Based on this result, approximate interpolants are given. They can approximate interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. They can uniformly approximate any C1 continuous function of one variable. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74581-5_32 | ISICA |
Keywords | Field | DocType |
inverse multiquadric activation function,approximate interpolate,single layer neural network,distinct data,approximate interpolants,approximate interpolation,distinct sample,c1 continuous function,single-hidden layer feedforward neural,arbitrary precision,inverse multiquadric function,neural network,activation function,feedforward neural network | Continuous function,Inverse,Feedforward neural network,Mathematical optimization,Arbitrary-precision arithmetic,Activation function,Interpolation,Algorithm,Artificial neural network,Mathematics | Conference |
Volume | ISSN | ISBN |
4683 | 0302-9743 | 3-540-74580-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
1 | ||