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.

中文翻译：

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用于变分问题的基于 Pade 近似的网络

在解决变分问题时，关键是有效地找到最小化或最大化指定函数的目标函数。在本文中，通过使用 Pade 近似，我们提出了一种解决变分问题的方法。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，我们表明该方法有效且高效地搜索目标函数。