1. The Least Mean Squares algorithm (LMS) SD研究的最陡下降方法是一种递归计算信号统计量已知时维纳滤波器的递归算法 (knowledge about R och p)。 问题是,这个信息通常是未知的! LMS是一种基于与最陡下降法相同的原理的方法,但其统计量是连续估计的。 由于统计量是连续估计的,因此LMS算法可以适应信号统计量...
This chapter develops an alternative to the method of steepest descent called the least mean squares (LMS) algorithm, which will then be applied to problems in which the second-order statistics of the signal are unknown. Due to its simplicity, the LMS algorithm is perhaps the most widely used...
3.3.1 Least mean square (LMS) The LMS algorithm adjusts the filter parameters in order to minimize the mean squares error between the filter output signal and the expectations output signals. LMS is based on a steepest descent algorithm. The updated filter coefficient for LMS algorithm is given...
The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has...
The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from a weighted sum of kernel functions evaluated at each incoming data sample. With time, the size of the filter as well as the computation and...
A two-dimensional adaptive algorithm which resists impulsive interference is presented. The proposed two-dimensional median least mean squares (TDMLMS) algorithm is a gradient-based steepest descent algorithm and employs the sample median of the instantaneous gradients within a suitable window as a ...
This paper compares the convergence rate performance of the normalized least-mean-square (NLMS) algorithm to that of the standard least-mean-square (LMS) algorithm, which is based on a well-known interpretation of the NLMS algorithm as a form of the LMS via input normalization. With this inte...
Two adaptive filters, such as, least-mean-square (LMS) and normalized-least-mean-square (NLMS) are applied to remove the noises. For better clarification simulation results are compared in terms of different performance parameters such as, power spectral density (PSD), spectrogram, frequency ...
From this point of view, it often outperforms, and by far, the family of least-mean-square (LMS) algorithms. On the other hand, the price to pay for this advantage is a significant increase in the computational complexity. Several interesting solutions to reduce the complexity of the RLS ...
Kernel least mean square algorithm with constrained growth The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from ... PP Pokharel,W Liu,JC Principe - 《Signal Processing》 被引量: 79发表: 2009年...