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convergence in mean相关的网络例句

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

Similarly, we discuss the relation between fundamental convergence in fuzzy measure and fuzzy mean fundamental convergence of fuzzy integrals.

类似地,还讨论了"依测度基本"与"广义模糊积分平均基本"的关系。

In the section, the definitions and main properties of the operator have been discussed systematically, the operational rules of one dimensional PGOPO have been strictly established based on the theory of convergence in the mean square, error and convergence analysis of approximation by PGOPO has been discussed.

系统地论述了分段广义正交多项式算子的定义、性质,并为一维PGOPO算子严格地建立一套在均方收敛意义下的代数运算规则,给出了分段广义正交多项式算子作用下的误差及收敛性分析结果。

The atmospheric mean horizonal convergence in two examples is calculated using the two modified EVAD methods,the convergence obtained is then adjusted with the variational method,and the vertical velocity is computed in ter...

根据这2种改善的EVAD方法,分别计算出大气平均散度,再用变分法对散度值进行调整,然后利用连续方程,计算大气的垂直速度,并对各种结果进行了比较分析。

In chapter 3, we study the relation between convergence in fuzzy measure of nonnegative fuzzy measurable functions and fuzzy mean convergence of fuzzy integrals.

研究了非负模糊可测函数列依测度收敛与广义模糊积分平均收敛之间的关系。

On the other hand, we also give several terse sufficiency conditions where the fuzzy mean convergence of fuzzy integrals implies the convergence in fuzzy measure and making them equivalent.

另一方面,文中还给出了广义模糊积分平均收敛蕴涵依测度收敛的几个简洁的充分性条件,以及使两者等价的条件。

Furthermore, as its corollary, we still get several terse and pragmatic sufficiency conditions where the convergence in fuzzy measure implies the fuzzy mean convergence of fuzzy integrals.

作为这一结果的推论,还得到了依测度收敛蕴涵广义模糊积分平均收敛的几个简洁、实用的充分性条件。

First, we present various new convergence concepts for sequence of fuzzy random variables, including convergence sure, convergence almost sure, uniform convergence, uniform convergence almost sure, almost uniform convergence, convergence in chance measure, and their corresponding weak convergence. Second, the relations among some types of convergence are studied. Finally, we design some algorithms about fuzzy random simulations to compute the mean chance of fuzzy random event, find the optimistic value of a return function, and evaluate the expected value of a fuzzy random variable.

首先,提出了几类模糊随机变量序列的收敛性概念,包括:必然收敛、几乎必然收敛、一致收敛、几乎必然一致收敛、近一致收敛、依机会测度收敛以及与以上概念相对应的弱收敛;其次,讨论了收敛性之间的关系;最后我们设计了模糊随机模拟算法,用于计算模糊随机事件的平均机会,寻找收益函数的乐观值,以及估计模糊随机变量的期望值。

Experiment in this paper validates the convergence of the improved Mean Shift algorithm, contrast convergence rate of improved Mean Shift algorithm with convergence rate of traditional Mean Shift algorithm.

本文的实验也进一步验证了改进的Mean Shift算法的收敛性,并对比了改进前后的Mean Shift算法的收敛速度。

The research of its convergence of Mean Shift algorithm is the foundation of its application. Comaniciu and Li Xiang-ru have respectively provided the proof for the convergence of Mean Shift but they both made a mistake in their proofs.

作为迭代算法,Mean Shift的收敛性研究是应用的基础,而Comaniciu和李乡儒分别证明了Mean Shift的收敛性,但证明过程存在错误。

In this paper, the imprecise proofs existing in some literatures are firstly pointed out. Then, the local convergence is proved in a new way and the condition of convergence to the local maximum point is offered. Finally, the geometrical counterexamples are provided for explanation about convergence of Mean Shift and the conclusion is further discussed.

首先指出了Comaniciu和李乡儒的证明过程存在错误;然后,从数学上重新证明了Mean Shift算法的局部收敛性,并指出其收敛到局部极大值的条件;最后,从几何上举反例分析了Mean Shift的收敛性,并进行了深入比较和讨论。

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