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fgn

  • 网络富贵鸟女鞋;分形高斯噪声;高斯噪声;噪声模型
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  1. A new method for distinguishing FBM from FGN is also presented in the paper .

    文中还提出了一种识别FBM和FGN的灰色判别新方法。

  2. G / D / 1 Queuing Simulation with FGN Input

    基于FGN输入流的G/D/1排队模拟

  3. Compared to other models , Durbin FGN model is more precise and stable .

    相比其它模型,DurbinFGN模型产生的业务比较稳定和准确。

  4. Most researchers regard the vertical distribution of well logging signal as fractal Gaussian noise ( fGn ) .

    许多研究认为,测井信号在纵向上的分布具有分数高斯噪音(fGn)特征。

  5. A new self-similar teletraffic generation arithmetic based on FGN and IDFT

    一种基于FGN和IDFT的自相似通信量生成算法

  6. Using ellipsometry , the adsorption behavior of FGN onto the polyurethane surface was preliminarily studied and compared with that on hydrophilic silicon surface .

    应用椭圆偏振术初步研究了FGN在聚氨酯表面的吸附行为,并与FGN在亲水硅片上的吸附行为进行了初步比较。

  7. In this paper , an iterative method is presented to estimate Hurst index , and it is applied to both FGN ( Fractional Gaussian Noise ) data and real traffic data .

    该文提出了一种快速估计Hurst指数的迭代算法,并将它应用于分形高斯噪声和真实网络流量数据。

  8. At equal protein concentration of 30 μ g / ml , both the adsorption speed and the final adsorbed amount of BSA were much less than those of FGN .

    BSA和FGN吸附液浓度同为30μg/ml时,BSA的吸附速率及饱和吸附量均大大低于FGN。

  9. It is shown that the final amount of adsorbed BSA is less than that of FGN at equal protein concentration when the concentration is above 5 μ g / ml.

    结果表明:蛋白质浓度大于5μg/ml时,相同蛋白质浓度下,BSA的最终吸附量小于FGN。

  10. A qualitative analysis of G / D / 1 queue with FGN input by computer simulation was presented , and attention was focused on the average delay time and the tail behavior of infinite and finite queues .

    通过实验仿真,主要考察平均等待时间和在队列分别为无限和有限情形下的队列尾部特征等参数,并对以FGN为输入流的G/D/1排队模型作了定性分析。

  11. Based on this , taking the fractal Gaussian noise ( fGn ) series with a prior known Hurst exponent into account , the performance of these Hurst algorithms impacting on the periodical signal and the Gaussian white noise respectively using reverse method is evaluated .

    在此基础上,以已知Hurst指数的分形高斯噪声(fGn)序列为主要研究对象,利用逆向方法,分别研究了周期信号以及高斯白噪声影响下的Hurst指数估计算法的估计性能。