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cotangent function相关的网络例句

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与 cotangent function 相关的网络例句 [注:此内容来源于网络,仅供参考]

A fixed point of a discrete-time neural network with generalized input-output function and by using the converse theorem of Lyapunov function we study the uniformly asymptotical stability of equilibrium in this discrete-time neural network with variable weight and give some sufficient conditions that guarantee the stability of it.

本文首先介绍了几类离散神经网络模型的由来及其研究概况,利用Schauder不动点原理证明了一类具有广义输入输出函数的离散神经网络模型平衡点的存在性,利用Lyapunov函数逆定理给出了这类离散神经网络模型在时变权值下的一致渐近稳定性的充分条件。

In this thesis, we firstly introduce several class of discrete-time neural network models and the research progress of the neural networks. By Schauder fixed-point principle we prove the existence of an equilibrium (i.e. a fixed point) of a discrete-time neural network with generalized input-output function and by using the converse theorem of Lyapunov function we study the uniformly asymptotical stability of equilibrium in this discrete-time neural network with variable weight and give some sufficient conditions that guarantee the stability of it.

本文首先介绍了几类离散神经网络模型的由来及其研究概况,利用Schauder不动点原理证明了一类具有广义输入输出函数的离散神经网络模型平衡点的存在性,利用Lyapunov函数逆定理给出了这类离散神经网络模型在时变权值下的一致渐近稳定性的充分条件。

But in our method, the reference function value of the iteration point is defined by a special weighted average, i.e., a convex combination of all previous function values.

而在此算法中,迭代点的参考函数值为一特殊的加权平均值,即前面所有点函数值的凸组合。

It is proved that strictly efficient point set of the objective space is connected when objective function is cone convex-like,and that if objective function is cone convex set-valued mapping,then strictly efficient solution set is also connected.

证明了当目标函数为锥类凸的集值映射时,其目标空间里的严有效点集是连通的;若目标函数为锥凸的集值映射时,其严有效解集也是连通的。

Convex function is an significant type of function.

凸函数是一类重要的函数。

The convex function is a kind of function with special properties.

凸函数则是一类性质独特的函数。

The decreasing conditions of an energy function are investigated by the use of convex function.

利用凸函数的定义研究了能量函数下降的条件,根据凸函数的性质分析它的共轭函数减去二次函数之差仍为凸函数的条件。

In this paper, A new global convex filled function and several concrete forms of a two-parameters filled function are given.

针对这些填充函数进行了分析证明和数值实验,结果说明了该填充函数的有效性。

To gain sevrial decision method of convex function by the definition of convex function and Taylor formula.

利用凸函数的定义和泰勒公式给出了凸函数的几个判别法。

Convex function plays an important role especilly in aspect of dipicting the function graphics and of deducing the inequality.

特别是在函数图形的描绘和不等式的推导方面,凸函数起着十分重要的作用。

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