在此基础上，又导出了单符号网络函数的计算公式。
On this basis, the formulas for calculating one-symbol network functions are given.
本文提出一种求符号网络函数的分类生成树组方法。
This paper presents a new method of generating tree-team classification for producing symbol network functions.
本文介绍应用网络函数概念建立状态方程的一种方法。
The paper introduces a kind of method about formulating state equation to apply the concept of network function.
本文给出了一种获得一般开关电容电路全符号网络函数的新方法。
A new method is presented for obtaining. Totally symbolic network functions of general switched-capacitor networks.
对生成符号网络函数的重要技术问题——冗余项问题进行深入分析。
The redundancy item problem which is an important technical problem of generating symbolic network function is discussed.
第三方应用程序和netsh命令都是基于公开的无线承载网络函数来实现的。
Third-party applications and the netsh commands are based on using the public wireless Hosted Network functions.
提出了一种基于网络函数的VF TO下变压器绕组响应计算的有效方法。
An efficient method is presented for calculating the response of transformer windings under VFTO based on network function in this paper.
符号网络函数，与网络的数值分析结果相比，往往可以更好地描述线性网络的性能。
The properties of linear networks can usually be characterized better by symbolic network functions than by numerical analysis results.
该方法可有效地避免在利用“树支组导纳积”构造符号网络函数过程中遇到冗余项问题。
The method can effectively avoid redundant team in the proceeding of structuring symbol network functions using "tree-team admittance product".
本文推导了网络函数及其导数的求值公式，应用这些公式可使网络分析及优化算法更有效。
Evaluating formulae of network functions and their derivatives are introduced in this paper. The algorithm of network analysis and optimization will be more efficient by using these formulae.
它利用了不定导纳矩阵的概念，在符号网络函数的产生中应用了代数法与符号编码相结合的方法。
It is based on the concept of indefinite admittance matrix, and USES an algebraic method of symbolic code in the generation of symbolic network functions.
也就是说我们只需研究其一维函数逼近能力，所得的结论完全适合于多维情形，该定理大大简化了前馈多层神经网络函数逼近问题的分析难度。
This theorem simplifies greatly the analysis of the function approximation ability of FFMLNN because one needs only to study the one dimensional function approximation ability of FFMLNN.
也就是说我们只需研究其一维函数逼近能力，所得的结论完全适合于多维情形，该定理大大简化了前馈多层神经网络函数逼近问题的分析难度。
This theorem simplifies greatly the analysis of the function approximation ability of FFMLNN because one needs only to study the one dimensional function approximation ability of FFMLNN.
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