- 更多网络例句与算子插值相关的网络例句 [注：此内容来源于网络，仅供参考]
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We posed the concept of sufficient intersection about s(1≤s≤n) algebraic hypersurfaces in n-dimensional space and proved the dimension of polynomial space Pm(which denotes the space of all multivariate polynomials of total degree≤m) on the algebraic manifold S=s(f1,…, fs) where f1(X=0,…, f s=0denote s algebraic hypersurfaces of sufficient intersection, then gave a convenient expression for dimension calculation by using the backw ard difference operator.
给出了n维空间中s(1≤s≤n)个代数超曲面充分相交的概念，证明了n元m次多项式空间Pm在充分相交的代数流形S=s(f1，…， fs)（f1=0，…， fs=0表示s个代数超曲面）上的维数，并利用倒差分算子给出一个方便计算的表达式；构造了沿代数流形上插值适定结点组的叠加插值法；证明了在充分相交的代数流形上任意次插值适定结点组的存在性；给出代数流形上插值适定结点组的性质和判定条件。
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In the third chapter, we obtain the average errors(asymptotic order or weakly asymptotic order) of the Lagrange interpolation sequence, the Hermite-Fejer interpolation sequence and the Hermite interpolation sequence based on the Chebyshev nodes on the 1-fold integrated Wiener space.
第三章分别给出了基于第一类Chebyshev多项式零点的Lagrange插值算子列、Hermite-Fejer插值算子列和Hermite插值算子列在1-重积分Wiener空间下的平均误差的值。
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In this paper ,on one hand ,we establish the weakly asymptotic order of the classical Bernstein interpolation sequence approximate functionin the Wiener space(or 1-fold integrated Wiener space),on the other hand,we discuss the asymptotically order for the average error of Lagrange interpolation sequence, Hermite-Fejer interpolation sequence and Hermite interpolation sequence based on the Chebyshev nodes on the 1-fold integrated Wiener space.
本文一方面确定了经典的Bernstein多项式算子列逼近函数时在Wiener空间(或1-重积分Wiener空间)下的平均误差的弱渐近阶；另一方面确定了基于第一类Chebyshev多项式零点的Lagrange插值算子列、Hermite-Fejer插值算子列和Hermite插值算子列在1-重积分Wiener空间下的平均误差的弱渐近阶。
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In Chapter Three, we use the method of interpolation spline of differential operater to come up with the reproducing kernel in H01 with respect to bounded linear operator in H10. Then we use the reproducing kernel to develop the expression of the best approximating of bounded linear operator in H10 and prove its convergence.
第三章中，对于H_0~1中的有界线性算子，用微分算子插值样条函数的方法给出了H_0~1空间中的再生核，利用此再生核给出了H_0~1上的有界线性算子的最佳逼近的表达形式，并证明了其收敛性。
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Research emphasis is put on thefollowing aspects:First,study of multiwavelet basis(e.g.periodic vector,interpolation vector,multi-dimensional vector)suitable for different practical demands.Second,from perspective of operatorapproximation order,study of normal function approximation operator,contrast and comparison ofwavelet operator and multiwavelet operator and their respective applicable fields.And further,onbasis of the above,exploration of approaches of multiwavelet analysis application in function spacetheoretical research,and higher-lever,more convenient approaches for multi-function spaceapproximation theoretical research.Third,by fully employing unique features of multiwavelet,research action of multiwavelet analysis in the construction of L〓space non-conditionalbasis.Fourth,research such as transient signal analysis,image edge extraction,datacompression,fractal signal analysis.
其一，研究适宜于不同实际问题需要的向量小波基（如周期向量小波、插值向量小波、高维向量小波等）；其二，从算子逼近阶的角度研究一般函数逼近算子、小波算子和向量小波算子的异同点以及较优适用领域；在此基础上，探索将向量小波分析应用于函数空间理论研究的途径，寻找更高层次、更便捷地研究多元函数空间逼近理论的方法；其三，充分利用向量小波所独具的完美性质，探索在〓空间〓无条件基的构造中，向量小波分析的价值；其四，对向量小波适用的信号瞬态分析、图像边缘分析、数据压缩保真、分形信号分析等领域应给予特别的重视。
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Thirdly,quasi-normal inequalities of a-variation maximal operator and a-conditional variation maximal operator of scalar predictable tree martingales areidentified by the use of martingale transforms and by the construction of convex or concave function method;on this basis and with the help of previsiblity or regu-larity,Burkholder-Davis-Gundy\'s inequality of a-variation maximal operator and a-conditional variation maximal operator of scalar predictable tree martingales are iden-tified by the application of Hardy-Lorentz interpolation theory.At the same time,bythe use of G.
再次，应用鞅变换和构造凸或凹函数方法证明了标量值可料树鞅的α-方极大算子和α-条件方极大算子的拟范数不等式；然后，在这些拟范数不等式的基础上，应用Hardy-Lorentz空间插值方法证明了当树鞅是可控或正规树鞅时关于标量值可料树鞅α-方极大算子和α-条件方极大算子的Burkholder-Davis-Gundy's不等式成立。
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In image processing, method as follows will be introduced in this paper: a method combining image threshold iterative segmentation with threshold interpolation, edge detection operator Sobel and LoG, edge linking method using delation operator based on mathematical morphology, using boundary tracking and projection method in edge distill process. Through this method, measurement of wheelset will be met precision demand.
本文阐明了图像的分通道自动采集过程，以及对采集到的原始图像进行预处理过程，达到图像去噪声的目标，本课题采用了阈值分割中迭代阈值和阈值插值相结合的方法，Sobel算子、LoG算子边缘检测算法，基于数学形态学的膨胀运算子进行边缘断点连接以及目标提取中的投影法和边缘跟踪方法，使得提取轮对图像边缘达到测量精度的要求。
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As to how to avoid weaknesses of these two operators, scholars have made unremitting efforts. One of the most famous is the French mathematician Sablonniere P, who introduced and studied a kind of new quasi-Bernstein interpolation operators in 1992. This kind of operators have given dual attention to the Lagrange operator and the Bernstein operator merit, and have avoided the twos deficiency.
对于这两种算子如何扬长避短，学者们做了不懈努力，其中最为著名的是法国数学家Sablonniere P，他于1992年引入并研究了一种新的拟Bernstein插值算子B上标(k 下标 n，，这是一类介于Lagrange算子与Bernstein算子之间的拟插值算子，这类算子兼顾了Lagrange算子与Bernstein算子的优点，克服了二者的不足。
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Fourthly,in the UMD spaces,the tree martingale transforms and their maximaloperators are defined,and then based on the geometrical property of UMD spaces andcombining new interpolation techniques of Hardy-Lorentz spaces,the inequality ofUMD space valued tree martingale transform maximal operators is obtained.
然后，在UMD空间中，我们定义了树鞅鞅变换及其极大算子，并用UMD空间几何性质和Hardy-Lorentz空间的算子插值理论证明了UMD空间值树鞅鞅变换的极大算子不等式。
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From our results we know that the average error of the Lagrange interpolation sequence and the Hermite interpolation sequence based on the Chebyshev nodes in the 1-fold integrated Wiener space equal weakly to the average error of their corresponding optimal approximation polynomial in the 1-fold integrated Wiener space,and as a kind of information-based operation,they have simple form and their recover functions are polynomials,in the 1-fold integrated wiener space,their average error equal weakly to the corresponding minimal information radius whose permissible information operators class is function values.
通过我们的结果可以知道，基于第一类Chebyshev多项式零点的Lagrange插值算子列和Hermite插值算子列在1-重积分Wiener空间下的平均误差弱等价于相应的最佳逼近多项式在1-重积分Wiener空间下的平均误差，并且作为形式简单且恢复函数为多项式的一种信息基算法，其在1-重积分Wiener空间下的平均误差弱等价于相应的以函数值计算为可允许信息算子的最小平均信息半径。
- 更多网络解释与算子插值相关的网络解释 [注：此内容来源于网络，仅供参考]
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central difference notation：中心差分记法
central difference interpolation 中心差分插值法 | central difference notation 中心差分记法 | central difference operator 中心差分算子
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interpolation method：插值法
interpolation function 插值函数 | interpolation method 插值法 | interpolation of operators 算子插值
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interpolation of operators：算子插值
插值公式 interpolation formula | 算子插值 interpolation of operators | 插值问题 interpolation problem
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interpolation polynomial：插值多项式
interpolation of operators 算子插值 | interpolation polynomial 插值多项式 | interpolation problem 插值问题
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interpolation problem：插值问题
算子插值 interpolation of operators | 插值问题 interpolation problem | 插值求积公式 interpolation quadrature formula