A variable projection method for the general radial basis function neural network
From MaRDI portal
Publication:6160604
DOI10.1016/j.amc.2023.128009OpenAlexW4362509393MaRDI QIDQ6160604
Publication date: 26 June 2023
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2023.128009
shape parameterradial basis function neural networkvariable projectionseparable nonlinear least squaresradial basis function least squares
Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Mathematical programming (90Cxx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction
- The mystery of the shape parameter. III
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- High accurate finite differences based on RBF interpolation and its application in solving differential equations
- On the search of the shape parameter in radial basis functions using univariate global optimization methods
- Variable projection for nonlinear least squares problems
- On choosing ``optimal shape parameters for RBF approximation
- Optimization theory and methods. Nonlinear programming
- Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series
- Introduction to Nonlinear Optimization
- Developments of NEWUOA for minimization without derivatives
- A variable projection method for solving separable nonlinear least squares problems
- Separable nonlinear least squares: the variable projection method and its applications
- The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
This page was built for publication: A variable projection method for the general radial basis function neural network