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Distribution of absolute maximum of mean square differentiable Gaussian stationery process

机译:均方可微高斯文具过程的绝对最大值的分布

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In this paper it is obtained the distribution of absolute maximum of mean square differentiable stationery Gaussian process by means of integration of the results of the second Kolmogorov equation solution. It is shown the way simplifying integration and its interrelation to integro-differential equation obtained before. The second Kolmogorov equation is solved first for the boundary conditions allowing to obtain the results in form of infinite series with coefficients obtained by means of solution of Sturm-Liouville problem and reducing to the simple expression. It is analyzed the correlation of obtained results with known before. It is carried out a comparative analysis of correlation functions and expressions for distribution of absolute maximums of mean square differentiable and single-component Markov processes. In spite of correlation function of single-component Markov process can be considered as limit expression for correlation function of mean square differentiable process, the expression for distribution of their absolute maximums are essentially different. It shows practical meaning of the results since real processes in radio engineering systems can be mean square differentiable only.
机译:本文通过对第二个Kolmogorov方程解的结果进行积分,获得了均方可微文具高斯过程的绝对最大值的分布。它显示了简化积分的方法及其与之前获得的积分微分方程的相互关系。首先针对边界条件求解第二个Kolmogorov方程,从而可以得到无穷级数形式的结果,其系数通过Sturm-Liouville问题的解得到,并简化为简单表达式。分析获得的结果与以前已知的相关性。进行了相关函数和表达式的比较分析,用于均方可微和单分量Markov过程的绝对最大值的分布。尽管单成分马尔可夫过程的相关函数可以被认为是均方可微过程的相关函数的极限表达式,但它们的绝对最大值的分布表达式本质上是不同的。它显示了结果的实际意义,因为无线电工程系统中的实际过程只能是均方差。

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