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mean convergence相关的网络例句

查询词典 mean convergence

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

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。

Construction and properties of the integral; convergence theorems; Radon-Nykodym theorem; Fubini's theorem; mean convergence.

积分。 积分的构造和性质;各类收敛定理;拉东(Radon-Nykodym)定理;富比尼定理;平均收敛

Using this inequality one can prove that the mean convergence of positive martingale implies its point wise convergence.

在这一极大不等式的基础上可以证明正值鞅的均收敛蕴涵了它的点收敛。

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。

In this paper, the theory, algorithm, and experiment of automatic object detection and tracking are studied in depth. It is firstly pointed out that the essential of Mean Shift method is a special Newton-Gaussian method. A new method named Fast Mean Shift is established to stretch the conservative step of Mean Shift method. The convergence and validity of this new method are proved in theory. And it is also proved that the convergence speed of Fast Mean Shift is faster than that of Mean Shift. The contrast experiments of searching the maximum possibility density of random of data sets in plane and 3D space are done. The results show that this new method can reduce the iterations greatly. A new object tracking method based on Fast Mean Shift is built to improve the object tracking performance, which is shown in the face tracking experiment with the tennis sequence provided by the Ohio State University, and the car tracking experiment with the car sequence provided by Kalsruhe University. The face trcking experiment with highly noised images show that the object tracking method based on Fast Mean Shift has strong anti-jamming ability. A new fast color object detection technology based on characteristic color is established, which use characteristic color distribution to compute the characteristic color vector of any area in an image quickly. With the high performance search method, the fast object detection is achieved. At last, using object tracker based on Fast Mean Shift and color object detector based on characteristic color with the Kalman filter, PID controller, searial communication and other technologies, automatic object detection and tracking system with control system is built. The availability and anti-jamming ability of this system are verified by some object detection and tracking tests in different scenes.

本文对目标自动识别与跟踪进行了理论、计算、试验三方面的深入研究,主要包括:首次指出了目标跟踪技术中常用的均值迁移方法的本质为一种特殊的高斯-牛顿方法,改进了均值迁移方法步长取值保守的弱点,建立了快速均值迁移方法,证明了该方法的收敛性、有效性以及收敛速度优于均值迁移方法;进行了平面和3维随机分布数据集的最大概率密度搜索对比试验,试验结果表明,快速均值迁移方法大大减少了迭代次数;建立了基于快速均值迁移的目标跟踪方法,利用俄亥俄州立大学提供的乒乓球序列图像和卡斯鲁厄大学的汽车序列图像,对人脸和汽车目标跟踪性能分别进行了对比试验,并进行了高噪声人脸图像目标跟踪试验,结果表明,基于快速均值迁移的目标跟踪方法有效提高了目标跟踪性能,具有很强的抗干扰能力;建立了一种新型彩色目标自动识别方法,采用特征色彩分布函数实现了对任意图像区域特征色彩矢量的快速计算,建立了高效的搜索方法,实现了彩色目标的快速识别;将基于快速均值迁移方法的目标跟踪方法、基于特征色彩的目标识别方法与卡尔曼滤波、PID控制、串行通讯等技术结合,建立了带有控制系统的快速目标自动识别与跟踪系统,并在不同场景下进行了目标自动识别与跟踪试验,验证了快速目标自动识别与跟踪系统的有效性和抗干扰能力。

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