Convergence of the delayed normalized LMS algorithm with decreasing step size

1996 ◽  
Vol 44 (12) ◽  
pp. 3008-3016 ◽  
Author(s):  
Sang-Sik Ahn ◽  
P.J. Voltz
Keyword(s):  
Lms Algorithm ◽  
Step Size ◽  
Normalized Lms

scholarly journals A New Variable Step-Size NLMS Algorithm and its Performance Evaluation in Echo Cancelling Applications

2011 ◽  
Vol 9 (03) ◽  
Author(s):  
F. M. Casco-Sánchez ◽  
R. C. Medina-Ramírez ◽  
M. López-Guerrero
Keyword(s):  
Cross Correlation ◽  
Echo Cancellation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filter Output ◽  
Nlms Algorithm ◽  
Variable Step ◽  
Size Adjustment ◽  
Normalized Lms

In this work we introduce a variable step-size normalized LMS algorithm for adaptive echo cancellation in a FIRstructure. In the proposed scheme, the step-size adjustment is controlled by using the square of the cross-correlationbetween the squared output error and the adaptive filter output. The proposed algorithm (that we call VSSSC aftervariable step size based on the squared cross-correlation) was evaluated using white noise and speech signals.Simulation results show that our proposal achieves better performance than similar algorithms in single and doubletalk. The proposed algorithm can be used in a number of applications such as in echo reduction for long-haul voicecommunications.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

Designs ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 65
Author(s):  
Amritha Kodakkal ◽  
Rajagopal Veramalla ◽  
Narasimha Raju Kuthuri ◽  
Surender Reddy Salkuti
Keyword(s):  
Wind Power ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Normalized Lms

A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

Simulation Realization of Adaptive Filtering Based on a Normalized LMS Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1159-1163
Author(s):  
Ya Feng Li ◽  
Zi Wei Zheng
Keyword(s):  
Adaptive Filtering ◽  
Notch Filter ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Adaptive Notch Filter ◽  
Variable Step ◽  
Simulation Results ◽  
Normalized Lms

This paper presents the new algorithm which is an improved normalized variable step size LMS adaptive filtering algorithm. A normalized LMS algorithm with variable step size iterative formula is deduced and at the same time the simulation results prove that the new algorithm has good performance. The LMS adaptive filtering algorithm has been widely used in many applications such as system identification, noise cancellation and the adaptive notch filter ,the paper analyses the application and implement the simulation by matlab. the result shows the proposed algorithm has been applied well.

scholarly journals Set-Membership Quaternion Normalized LMS Algorithm

2018 ◽  
Author(s):  
Hamid Reza Moradi ◽  
Akram Zardadi
Keyword(s):  
Wind Profile ◽  
Adaptive Beamforming ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Set Membership ◽  
Normalized Least Mean Square ◽  
Update Rate ◽  
Normalized Lms

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

scholarly journals Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size

1998 ◽  
Vol 5 (5) ◽  
pp. 111-114 ◽  
Author(s):  
S. Gollamudi ◽  
S. Nagaraj ◽  
S. Kapoor ◽  
Yih-Fang Huang
Keyword(s):  
Lms Algorithm ◽  
Step Size ◽  
Adaptive Step Size ◽  
Set Membership ◽  
Adaptive Step ◽  
Normalized Lms

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

scholarly journals Modified Filtered-X Hierarchical LMS Algorithm with Sequential Partial Updates for Active Noise Control

2020 ◽  
Vol 11 (1) ◽  
pp. 344
Author(s):  
Pedro Ramos Lorente ◽  
Raúl Martín Ferrer ◽  
Fernando Arranz Martínez ◽  
Guillermo Palacios-Navarro
Keyword(s):  
Computational Complexity ◽  
Convergence Rate ◽  
Noise Control ◽  
Active Noise Control ◽  
Adaptive Strategy ◽  
Lms Algorithm ◽  
Step Size ◽  
Periodic Disturbances ◽  
Active Noise ◽  
Dependent Parameter

In the field of active noise control (ANC), a popular method is the modified filtered-x LMS algorithm. However, it has two drawbacks: its computational complexity higher than that of the conventional FxLMS, and its convergence rate that could still be improved. Therefore, we propose an adaptive strategy which aims at speeding up the convergence rate of an ANC system dealing with periodic disturbances. This algorithm consists in combining the organization of the filter weights in a hierarchy of subfilters of shorter length and their sequential partial updates (PU). Our contribution is threefold: (1) we provide the theoretical basis of the existence of a frequency-dependent parameter, called gain in step-size. (2) The theoretical upper bound of the step-size is compared with the limit obtained from simulations. (3) Additional experiments show that this strategy results in a fast algorithm with a computational complexity close to that of the conventional FxLMS.

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