Title :
Joint conditional and steady-state probability densities of weight deviations for proportionate-type LMS algorithms
Author :
Kevin T. Wagner;Miloš I. Doroslovački
Author_Institution :
Naval Research Laboratory, Radar Division, Washington, DC 20375, USA
Abstract :
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.
Keywords :
"Steady-state","Joints","Vectors","Covariance matrix","Noise measurement","Noise","Probability density function"
Conference_Titel :
Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on
Print_ISBN :
978-1-4673-0321-7
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2011.6190326
