Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data

1990 ◽  
Vol 37 (11) ◽  
pp. 1367-1374 ◽  
Author(s):  
E. Eweda
Keyword(s):  
Adaptive Filtering ◽  
Lms Algorithm ◽  
Analysis And Design ◽  
Gaussian Data

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

scholarly journals Development of Multi-Staged Adaptive Filtering Algorithm for Periodic Structure-Based Active Vibration Control System

2019 ◽  
Vol 9 (3) ◽  
pp. 611 ◽  
Author(s):  
Qiu Yang ◽  
Kyeongnak Lee ◽  
Byeongil Kim
Keyword(s):  
Adaptive Filtering ◽  
Periodic Structures ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Complex Frequency ◽  
Step Size ◽  
Initial State ◽  
Frequency Spectra ◽  
Filtering Algorithm ◽  
Active Vibration

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles.

Adaptive filtering for intelligent sensing speech based on multi-rate LMS algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 1493-1503 ◽  
Author(s):  
Ranran Liu ◽  
Hongxiang Xu ◽  
Enxing Zheng ◽  
Yifeng Jiang
Keyword(s):  
Adaptive Filtering ◽  
Lms Algorithm

The P -Norn Generalization of the LMS Algorithm for Adaptive Filtering

2003 ◽  
Vol 36 (16) ◽  
pp. 1717-1722 ◽  
Author(s):  
Jyrki Kivinen ◽  
Manfred K. Warmuth ◽  
Babak Hassibi
Keyword(s):  
Adaptive Filtering ◽  
Lms Algorithm

Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms

2016 ◽  
Vol 20 (7) ◽  
pp. 1119-1126 ◽  
Author(s):  
Chen Ye ◽  
◽  
Guan Gui ◽  
Shin-ya Matsushita ◽  
Li Xu ◽  
...  
Keyword(s):  
Convex Optimization ◽  
Adaptive Filtering ◽  
Large Scale ◽  
Block Structure ◽  
Signal Reconstruction ◽  
Lms Algorithm ◽  
Sparse Signal ◽  
Sparse Signals ◽  
Filtering Algorithms ◽  
Sparse Signal Reconstruction

Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “blockℓ0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.

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