The filled function method is an approach for solving unconstrained global minimization problem.
填充函数法是一种解无约束全局极小化问题的方法。
This paper proposes a new global optimization technique in which combines the filled function method and BP algorithm for Training feedforward neural networks.
把填充函数法与BP算法相结合，提出一种训练前向神经网络的混合型全局优化新算法。
The properties of this filled function and a global minimization method based on the filled function are analyzed.
给出一类求解总体极值问题的填充函数，分析了该填充函数的特性与基于填充函数的总体极小化方法。
This paper presents a neighborhood search algorithm for finding a local minimal solution of the problem, then a filled function method for solving 0-1 programming is proposed.
文章首先给出搜索0-1规划局部极小解的邻域搜索算法，在此基础上给出了填充函数算法。
This paper presents a neighborhood search algorithm for finding a local minimal solution of the problem, then a filled function method for solving 0-1 programming is proposed.
文章首先给出搜索0-1规划局部极小解的邻域搜索算法，在此基础上给出了填充函数算法。
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