The Pade Approximant Based Network for Variational Problems
Numerical Analysis
2020-04-03 v1 Numerical Analysis
Signal Processing
Abstract
In solving the variational problem, the key is to efficiently find the target function that minimizes or maximizes the specified functional. In this paper, by using the Pade approximant, we suggest a methods for the variational problem. By comparing the method with those based on the radial basis function networks (RBF), the multilayer perception networks (MLP), and the Legendre polynomials, we show that the method searches the target function effectively and efficiently.
Cite
@article{arxiv.2004.00711,
title = {The Pade Approximant Based Network for Variational Problems},
author = {Chi-Chun Zhou and Yi Liu},
journal= {arXiv preprint arXiv:2004.00711},
year = {2020}
}