JOINT CONDITIONAL AND STEADY-STATE PROBABILITY DENSITIES OF WEIGHT DEVIATIONS FOR PROPORTIONATE-TYPE LMS ALGORITHMS

被引：0

作者：

Wagner, Kevin T. ^{[1
]}

Doroslovacki, Milos I. ^{[2
]}

机构：

[1] Naval Res Lab, Div Radar, Washington, DC 20375 USA

[2] George Washington Univ, Dept Elect & Comp Engn, Washington, DC 20052 USA

来源：

2011 CONFERENCE RECORD OF THE FORTY-FIFTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (ASILOMAR) | 2011年

关键词：

Adaptive Estimation; Adaptive Filters; Least Mean Square Methods;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, the conditional probability density function of the current weight deviations given the preceding weight deviations is generated for a wide array of proportionate type least mean square algorithms. Additionally, the application of using the conditional probability density function to calculate the steady-state joint conditional probability density function is examined along with several examples showing the feasibility of the approach. In the process of calculating the steady-state joint conditional probability density function a proof showing that the weight deviation vectors form a Markov chain is presented.

引用

页码：1775 / 1779

页数：5

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