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...
NLMS (Normalized Least Mean Squares) 是LMS (Least Mean Squares) 算法的一种改进。与LMS算法相比,NLMS算法具有更快的收敛速度和较小的稳态误差。NLMS算法的更新公式为: w(n+1)=w(n)+μe(n)x(n)||x(n)||2+ϵ 其中, :当前时刻的权重向量。w(n):当前时刻的权重向量。
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 measure of the true gradient. The nonlinear action of the median filtering operation ...
Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB covers the core concepts of t... Poularikas,Alexander - CRC Press, Inc. 被引量: 4发表: 2014年 Study of nonlinear variants of the least mean square (LMS) algorithm The class of LMS-type algorithms utilizing zero-memory ...
Least Mean Squares Regression(一) 1. Examples 假设我们想从一辆汽车的重量和年龄来预测它的里程数: 我们想要的是:一个可以使用x1x1和x2x2来预测里程的function。 线性回归:利用线性模型预测连续值的策略 假设:输出是输入的线性函数 Mileage=w0+w1⋅x1+w2⋅x2Mileage=w0+w1⋅x1+w2⋅x2 学习:利用...
U.M. Al-Saggaf, M. Moinuddin, M. Arif, A. Zerguine, The q-least mean squares algorithm. Signal Process. A.R.A.L. Ali, V. Gupta, R.P. Agarwal, A. Aral, V. Gupta,Applications of q-Calculus in Operator Theory(Springer, New York, 2013) ...
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 block assumes the random errors Δxn and Δyn follow a zero-mean normal distribution. The block assumes that the weights σ2xn and σ2yn are known. In the RTLS algorithm, the scaling factor k must proportionally relate the weights σ2xn and σ2yn such that σxn=kσyn ...
A new technique for the least-mean-squares (LMS) phase-unwrapping method is developed that incorporates the concept of branch cuts between phase singularities (residues), which are usually associated with the path-following gradient integration technique. These branch cuts are introduced by decomposition...