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priori information中文是什么意思

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用"priori information"造句"priori information"怎么读"priori information" in a sentence

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例句与用法

  • The iteration formula and determination of iteration step are set up according to a priori information of rock and concrete
    建立了考虑先验信息的修正高斯-牛顿算法的迭代格式和迭代步长的确定方法。
  • In order to reduce measure error causing by nlos , it adopt and regard intensity of the signal as right value , construct some tdoa measurement value again with the priori information
    为了减少非视距传播nlos引起定位参数测量误差,采用以信号强度为权值,用先验信息重构部分tdoa测量值。
  • Under a certain conditions on variance matrix invertibility , we show that the optimally weighted ls estimate outperforms the linear minimum variance estimate provided that they have the same priori information
    因此,我们讨论了在相同已知信息的情况下,即最优加权最小二乘估计也利用有关被估参数的先验信息时,二者的估计性能。
  • Blind beamforming ( bbf ) is concerned with the reconstruction of source signals from the outputs of a sensor array without a priori information of the direction of the desired source , relying instead on signal characteristics and array structures
    盲波束形成无需知道目标波达方向等先验信息,直接利用信号、基阵自身的特性,就可从基阵的输出采样信号中恢复出期望信号。
  • For the inverse method research , this paper proposes a truly original concept : simulated priori - information , which means using numerical simulation method to quest for priori information . this method greatly enriches the priori information on material parameters
    在反求技术方面,本文首次提出了仿真先验信息的概念,即用仿真方法获取先验信息,极大地丰富了获取材料参数先验信息的途径。
  • For a general linear model ( input matrix is deterministic ) , under a certain conditions on variance matrix invertibility , the two estimates can be identical provided that they have the same priori information on the parameter under estimation . even if the above information is unknown only for the optimally weighted ls estimate , the sufficient condition and necessary condition , under which the two estimates are identical , is derived . more significantly , we know how to design input of the linear system to make the performance of the optimally weighted ls estimation identical to that of the linear minimum variance estimation in case of being lack of prior information
    在一般线性模型(即输入矩阵为确定性)下,当两种估计都利用有关被估参数的先验信息时,二者在方差阵可逆的一定条件下可达到一致;当最优加权最小二乘估计不利用此先验信息时,存在二者一致的充分条件和必要条件,进而找到一种设计输入矩阵的方法,使得在先验信息缺乏的条件下,仍可利用最优加权最小二乘估计达到与线性最小方差估计一样优越的估计性能。
  • A practical approach has been brought forward that the priori information of signal and the external parameter do n ' t needed , on the basis of summarizing the methods of white noise elimination with wavelet and the different lipschtiz exponents and characters in time - scale plane of white noise and pd
    本论文在总结各类小波去除局放信号中白噪方法的基础上,根据白噪和局放信号的不同李氏指数以及在时间一尺度平面上的不同特征,提出了一种不需信号的先验知识和外部参数设定的适合局部放电信号处理的实用方法。
  • A method based on gene expression programming ( gep ) for identifying the nonlinear system model is presented , which makes up the insufficiency that traditional identification methods need much a priori information , and has a tidier and more efficient system model expression mode than genetic programming ( gp )
    摘要给出了利用基因表达编程( gep )进行非线性系统辨识的方法,弥补了传统辨识方法需要过多预知信息的不足,有着比遗传编程( gp )更简洁有效的系统模型结构表达方式。
  • In designing a multi - structuring elements filter , combination rules and structuring elements of the morphological transform are determined automatically , and one kind of neural networks is taken for the filter , in optimzing structural parameters of the filter , three computation methods are designed respectively , by adopting some priori information in application fields to guide optimal structural parameter learning procedure , which are the bp adaptive learning algorithm , the heuristic genetic learning algorithm and the inductive simulated annealing learning algorithm
    在多结构基元滤波器设计中,通过学习人-机交互选定的目标样本,自动确定形态变换的组合规则及其结构元素,最终以神经网络形式构成滤波器。在结构参数的优化学习中,利用应用领域的先验知识,分别设计了自适应bp学习、启发式遗传学习和引导式模拟退火学习等三种最优化计算方法。
  • The theoretical model calculation shows that , under the condition without any priori information , the energy weight value resolved from the continuation field or the maximum characteristic value of the matrix related to the continuation field can be used to judge the position of the potential field source , thus determining the burial depth of the geological body
    理论模型计算表明:在没有任何先验信息的条件下,可以用延拓场分解的能量权值异常或延拓场相关矩阵的最大特徵值,判断位场场源所在位置,从而能确定地质体的埋深。
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