论文标题
基于幻想问题的基于幻想的网络
The Pade Approximant Based Network for Variational Problems
论文作者
论文摘要
在解决变异问题时，关键是有效地找到最小化或最大化指定功能的目标函数。在本文中，通过使用涂片近似，我们建议一种解决变化问题的方法。通过将方法与基于径向基函数网络（RBF），多层感知网络（MLP）和Legendre多项式的方法进行比较，我们表明该方法可以有效，有效地搜索目标函数。
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.
