基于Pade逼近的变分问题网络
The Pade Approximant Based Network for Variational Problems
摘要：
在求解变分问题时，关键是有效地找到使指定泛函最小化或最大化的目标函数。本文利用Pade逼近给出了一种求解变分问题的方法。通过与基于径向基函数网络（RBF）、多层感知网络（MLP）和勒让德多项式的方法进行比较，表明该方法能够有效地搜索目标函数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
