scholarly journals An Alternative Approach to Obtain a New Gain in Step-Size of LMS Filters Dealing with Periodic Signals

2021 ◽  
Vol 11 (12) ◽  
pp. 5618
Author(s):  
Pedro Ramos Lorente ◽  
Raúl Martín Ferrer ◽  
Fernando Arranz Martínez ◽  
Guillermo Palacios-Navarro
Keyword(s):  
Adaptive Filters ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Size Parameter ◽  
Step Size ◽  
Speed Reduction ◽  
Alternative Approach ◽  
The Matrix ◽  
Periodic Signals ◽  
Limiting Value

Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved as a valid strategy in the active control of periodic noise consisting of a sum of harmonics. The convergence analysis carried out here is based on the periodic nature of the input signal, which makes it possible to formulate the adaptive process with a matrix-based approach, the periodic least-mean-square (P-LMS) algorithm In this paper, we obtain the upper bound that limits the step-size parameter of the sequential PU P-LMS algorithm and compare it to the bound of the full-update P-LMS algorithm. Thus, the limiting value for the step-size parameter is expressed in terms of the step-size gain of the PU algorithm. This gain in step-size is the quotient between the upper bounds ensuring convergence in the following two scenarios: first, when PU are carried out and, second, when every coefficient is updated during every cycle. This step-size gain gives the factor by which the step-size can be multiplied so as to compensate for the convergence speed reduction of the sequential PU algorithm, which is an inherently slower strategy. Results are compared with previous results based on the standard sequential PU LMS formulation. Frequency-dependent notches in the step-size gain are not present with the matrix-based formulation of the P-LMS. Simulated results confirm the expected behavior.

scholarly journals Variable step-size LMS adaptive filters for CDMA multiuser detection

2004 ◽  
Vol 17 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Karen Egiazarian ◽  
Pauli Kuosmanen ◽  
Ciprian Bilcu
Keyword(s):  
Multiuser Detection ◽  
Adaptive Filters ◽  
Convergence Speed ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Trade Off ◽  
Variable Step ◽  
Code Division Multiple

Due to its simplicity the adaptive Least Mean Square (LMS) algorithm is widely used in Code-Division Multiple access (CDMA) detectors. However its convergence speed is highly dependent on the eigen value spread of the input covariance matrix. For highly correlated inputs the LMS algorithm has a slow convergence which require long training sequences and therefore low transmission speeds. Another drawback of the LMS is the trade-off between convergence speed and steady-state error since both are controlled by the same parameter, the step-size. In order to eliminate these drawbacks, the class of Variable Step-Size LMS (VSSLMS) algorithms was introduced. In this paper, we study the behavior of some algorithms belonging to the class of VSSLMS for training based multiuser detection in a CDMA system. We show that the proposed Complementary Pair Variable Step-Size LMS algorithms highly increase the speed of convergence while reducing the trade-off between the convergence speed and the output error.

Smart VRP-NLMS algorithm for estimation of power system frequency

2019 ◽  
Vol 38 (1) ◽  
pp. 362-381
Author(s):  
Seyed Reza Aali ◽  
Mohammad Reza Besmi ◽  
Mohammad Hosein Kazemi
Keyword(s):  
Convergence Rate ◽  
Frequency Estimation ◽  
Lms Algorithm ◽  
Positive Sequence ◽  
Mean Square ◽  
Least Mean Square ◽  
Size Parameter ◽  
Step Size ◽  
Content Type ◽  
Nlms Algorithm

Purpose The purpose of this paper is to study variation regularization with a positive sequence extraction-normalized least mean square (VRP-NLMS) algorithm for frequency estimation in a three-phase electrical distribution system. A simulation test is provided to validate the performance and convergence rate of the proposed estimation algorithm. Design/methodology/approach Least mean square (LMS) algorithms for frequency estimation encounter problems when voltage contains unbalance, sags and harmonic distortion. The convergence rate of the LMS algorithm is sensitive to the adjustment of the step-size parameter used in the update equation. This paper proposes VRP-NLMS algorithm for frequency estimation in a power system. Regularization parameter is variable in the NLMS algorithm to adjust step-size parameter. Delayed signal cancellation (DSC) operator suppresses harmonics and negative sequence component of the voltage vector in a two-phase Î ± β plane. The DSC part is placed in front of the NLMS algorithm as a pre-filter and a positive sequence of the grid voltage is extracted. Findings By adapting of the step-size parameter, speed and accuracy of the LMS algorithm are improved. The DSC operator is augmented to the NLMS algorithm for more improvement of the performance of this adaptive filter. Simulation results validate that the proposed VRP-NLMS algorithm has a less misalignment of performance with more convergence rate. Originality/value This paper is a theoretical support to simulated system performance.

Application of Improved LMS Adaptive Algorithm in Acoustics Pyrometry

2014 ◽  
Vol 672-674 ◽  
pp. 2025-2028
Author(s):  
Shi Ping Zhang ◽  
Guo Qing Shen ◽  
Lian Suo An
Keyword(s):  
Temperature Measurement ◽  
Discrete Cosine Transform ◽  
Time Delay Estimation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Boiler Furnace ◽  
Cosine Transform ◽  
Variable Step

Acoustic pyrometry is a comparatively advanced method of temperature measurement developed in recent years, which possesses the essential characteristics of traditional temperature measurement approach. Considering the interferences, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above. In order to make the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses the sliding forgetting-weighted window. The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

A Novel Design and FPGA Implementation of Filters Adapted Using LMS Variants

2018 ◽  
Vol 27 (08) ◽  
pp. 1850125
Author(s):  
Sakshi ◽  
Ravi Kumar
Keyword(s):  
Interference Cancellation ◽  
Adaptive Filters ◽  
Hardware Implementation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Silicon Area ◽  
Novel Structure ◽  
Wide Range ◽  
Field Programmable

Adaptive filters have wide range of applications in areas such as echo or interference cancellation, prediction and system identification. Due to high computational complexity of adaptive filters, their hardware implementation is not an easy task. However, it becomes essential in many cases where real-time execution is needed. This paper presents the design and hardware implementation of a variable step size 40 order adaptive filter for de-noising acoustic signals. To ensure an area efficient implementation, a novel structure is being proposed. The proposed structure eliminates the requirement of extra registers for storage of delayed inputs thereby reducing the silicon area. The structure is compared with direct-form and transposed-form structures by adapting the filter coefficients using four different variants of the least means square (LMS) algorithm. Subsequently, the filters are implemented on three different field programmable gate arrays (FPGAs) viz. Spartan 6, Virtex 6 and Virtex 7 to find out the best device family that can be used to implement an Adaptive noise canceller (ANC) by comparing speed, power and area utilization. The synthesis results clearly reveal that ANC designed using the proposed structure has resulted in a reduction in silicon area without incurring any significant overhead in terms of power or delay.

Adaptive Fourier linear combiner based on modified least mean Kurtosis algorithm for the processing of sinusoidal signals

2019 ◽  
pp. 014233121988917
Author(s):  
Engin Cemal Mengüç
Keyword(s):  
Cost Function ◽  
Design Procedure ◽  
Sinusoidal Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Step Size ◽  
Hand Tremor ◽  
The Mean ◽  
Sinusoidal Signals ◽  
Weight Coefficients

This study introduces an adaptive Fourier linear combiner (FLC) based on a modified least mean kurtosis (LMK) algorithm in order to effectively process sinusoidal signals, which we call FLC-LMK algorithm. In the design procedure of the proposed FLC-LMK algorithm, the classical kurtosis-based cost function is first modified for only sinusoidal signal distributions instead of Gaussian. Then, the FLC-LMK algorithm is derived from the minimization of this cost function and thus updates the weight coefficients of the FLC structure so as to directly process sinusoidal signals. Moreover, in this study, the convergence in the mean of the proposed FLC-LMK algorithm is analysed in order to determine the lower and upper bounds of its step size parameter. The most important contributions of the use of the proposed algorithm in the FLC structure are that it increases the convergence rate, decreases the steady-state error level and also has a robust behaviour against sinusoidal signal distributions due to its modified cost function. The performance of the proposed FLC-LMK algorithm is evaluated on the synthetic and real-world pathological hand tremor data by comparing with that of the FLC based on the classical least mean square (LMS) (FLC-LMS) algorithm. The simulation results support the mentioned properties of the proposed FLC-LMK algorithm.

scholarly journals Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

2018 ◽  
Vol 7 (2.17) ◽  
pp. 79
Author(s):  
Jyoshna Girika ◽  
Md Zia Ur Rahman
Keyword(s):  
Steady State ◽  
Speech Enhancement ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Union Rate ◽  
Step Size ◽  
Adaptive Noise

Removal of noise components of speech signals in mobile applications  is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault.  The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWNLMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI). 

scholarly journals New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

2018 ◽  
Vol 38 (1) ◽  
pp. 187-198 ◽  
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Zhiyuan Gao ◽  
Jiaming Hu ◽  
Jian Wu
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Large Step ◽  
Variable Step ◽  
Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

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