Analysis of Bi-directional Filtered-x Least Mean Square Algorithm
نویسندگان
چکیده
منابع مشابه
Mean square convergence analysis for kernel least mean square algorithm
In this paper, we study the mean square convergence of the kernel least mean square (KLMS). The fundamental energy conservation relation has been established in feature space. Starting from the energy conservation relation, we carry out the mean square convergence analysis and obtain several important theoretical results, including an upper bound on step size that guarantees the mean square con...
متن کاملLeast Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملLeast Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملKernel Least Mean Square Algorithm
A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...
متن کاملPerformance Analysis of $l_0$ Norm Constraint Least Mean Square Algorithm
As one of the recently proposed algorithms for sparse system identification, l0 norm constraint Least Mean Square (l0-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of l0-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents compre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korea Society of Digital Industry and Information Management
سال: 2014
ISSN: 1738-6667
DOI: 10.17662/ksdim.2014.10.1.133