Статті в журналах: "LMS ALGORITHMS"

1

Liu, Ningning, Yuedong Sun, Yansong Wang, Hui Guo, Bin Gao, Tianpei Feng, and Pei Sun. "Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms." Noise Control Engineering Journal 67, no. 6 (November 1, 2019): 405–14. http://dx.doi.org/10.3397/1/376737.

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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.

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2

Ying-Chang Liang and F. P. S. Chin. "Coherent LMS algorithms." IEEE Communications Letters 4, no. 3 (March 2000): 92–94. http://dx.doi.org/10.1109/4234.831035.

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3

Kostromitsky, S. M., I. N. Davydzenko, and A. A. Dyatko. "Equivalent forms of writing of processing algorithms of adaptive antenna array." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 67, no. 2 (July 2, 2022): 230–38. http://dx.doi.org/10.29235/1561-8358-2022-67-2-230-238.

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The article is devoted to obtaining equivalent forms of writing of processing algorithms for the operation of adaptive antenna arrays, considering algorithms as varieties of some generalized LMS algorithm. This will facilitate a comparative analysis of the algorithms’ characteristics. The following algorithms of operation are considered: LMS, NLMS, LMS-Newton, SMI, RLS. The article contains the initial operation algorithms of adaptive antenna arrays, conclusions of equivalent processing algorithms and an equivalent block diagram of the generalized LMS algorithm. Equivalent forms of writing the operation algorithms of adaptive antenna arrays and their parameters are also presented in tabular form. Of particular interest is the equivalent operation algorithm in the case of the SMI algorithm, which differs most from the LMS algorithm. Equivalent algorithms differ only by the scalar convergence coefficient and the matrix normalizing factor. For LMS-Newton, SMI, and RLS algorithms, the matrix normalizing factor is the same, it is determined by inverting the estimation of the correlation matrix of input signals and reduces the dependence of the characteristics of the algorithms on the parameters of the correlation matrix. The scalar convergence coefficient of equivalent algorithms in the case of SMI and RLS algorithms depends on the iteration number and tends to zero for the SMI algorithm and to some non-zero value for the RLS algorithm. The dependence of the convergence coefficient on the iteration number makes it possible to optimize the characteristics of the algorithms at the transition stage. The tendency of the convergence coefficient to zero in the case of the SMI algorithm makes it effective only for stationary input signals. The non-zero steady-state value of the convergence coefficient in the case of the RLS algorithm allows its effective use in a non-stationary environment.

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4

Godavarti, M., and A. O. Hero. "Partial update LMS algorithms." IEEE Transactions on Signal Processing 53, no. 7 (July 2005): 2382–99. http://dx.doi.org/10.1109/tsp.2005.849167.

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5

Rusu, Alexandru-George, Silviu Ciochina, Constantin Paleologu, and Jacob Benesty. "An Optimized Differential Step-Size LMS Algorithm." Algorithms 12, no. 8 (July 24, 2019): 147. http://dx.doi.org/10.3390/a12080147.

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Adaptive algorithms with differential step-sizes (related to the filter coefficients) are well known in the literature, most frequently as “proportionate” algorithms. Usually, they are derived on a heuristic basis. In this paper, we introduce an algorithm resulting from an optimization criterion. Thereby, we obtain a benchmark algorithm and also another version with lower computational complexity, which is rigorously valid for less correlated input signals. Simulation results confirm the theory and outline the performance of the algorithms. Unfortunately, the good performance is obtained by an important increase in computational complexity. Nevertheless, the proposed algorithms could represent useful benchmarks in the field.

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6

Zhao, Sihai, Jiangye Xu, and Yuyan Zhang. "A Variable Step-Size Leaky LMS Algorithm." Wireless Communications and Mobile Computing 2021 (November 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/7951643.

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The leaky LMS algorithm has been extensively studied because of its control of parameter drift. This unexpected parameter drift is linked to the inadequacy of excitation in the input sequence. And generally leaky LMS algorithms use fixed step size to force the performance of compromise between the fast convergence rate and small steady-state misalignment. In this paper, variable step-size (VSS) leaky LMS algorithm is proposed. And the variable step-size method combines the time average estimation of the error and the time average estimation of the normalized quantity. Variable step-size method proposed incorporating with leaky LMS algorithm can effectively eliminate noise interference and make the early convergence, and final small misalignments are obtained together. Simulation results demonstrate that the proposed algorithm has better performance than the existing variable step-size algorithms in the unexcited environment. Furthermore, the proposed algorithm is comparable in performance to other variable step-size algorithms under the adequacy of excitation.

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7

Kalkar, Purvika, and John Sahaya Rani Alex. "FIELD PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF A VARIABLE LEAKY LEAST MEAN SQUARE ADAPTIVE ALGORITHM." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 69. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19566.

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Adaptive noise cancellation is an extensively researched area of signal processing. Many algorithms had been studied such as least mean square algorithm (LMS), recursive least square algorithm, and normalized LMS algorithm. The statistical characteristics of noise are fast in nature and the algorithms for noise cancellation should converge fast. Since LMS algorithm has slow convergence; in this paper, a variable leaky LMS (VLLMS) algorithm is explored. VLLMS is implemented using the concept of hardware-software cosimulation using Xilinx System Generator. The design is implemented on Virtex-6 ML605 field programmable gate array board. The implemented design is tested for sinusoidal signal added with an additivewhite Gaussian noise. The design summary and the utilization summary are presented.

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8

Chen, Yuan Yuan, Run Jie Liu, Jin Yuan Shen, and Dan Dan He. "The Use of Adaptive Algorithms on Smart Antenna Device." Advanced Materials Research 548 (July 2012): 730–34. http://dx.doi.org/10.4028/www.scientific.net/amr.548.730.

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Adaptive beamforming is one of the core technology of the smart antenna system. Two different adaptive algorithms which adopt the minimum mean square algorithm (LMS) and recursive least squares algorithm (RLS) are employed to realize the beamforming in smart antenna system. The smart antenna system based on LMS and RLS is simulated and realized by the MATLAB software in which a uniform linear adaptive antenna array is used. The results show that the smart antenna systems based on RLS and LMS algorithms can significantly reduce the bit error rate especially with the low SNR.

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9

Javed, Shazia, and Noor Atinah Ahmad. "Optimal preconditioned regularization of least mean squares algorithm for robust online learning1." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3375–85. http://dx.doi.org/10.3233/jifs-191728.

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Despite its low computational cost, and steady state behavior, some well known drawbacks of the least means squares (LMS) algorithm are: slow rate of convergence and unstable behaviour for ill conditioned autocorrelation matrices of input signals. Several modified algorithms have been presented with better convergence speed, however most of these algorithms are expensive in terms of computational cost and time, and sometimes deviate from optimal Wiener solution that results in a biased solution of online estimation problem. In this paper, the inverse Cholesky factor of the input autocorrelation matrix is optimized to pre-whiten input signals and improve the robustness of the LMS algorithm. Furthermore, in order to have an unbiased solution, mean squares deviation (MSD) is minimized by improving convergence in misalignment. This is done by regularizing step-size adaptively in each iteration that helps in developing a highly efficient optimal preconditioned regularized LMS (OPRLMS) algorithm with adaptive step-size. Comparison of OPRLMS algorithm with other LMS based algorithms is given for unknown system identification and noise cancelation from ECG signal, that results in preference of the proposed algorithm over the other variants of LMS algorithm.

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10

Egiazarian, Karen, Pauli Kuosmanen, and Ciprian Bilcu. "Variable step-size LMS adaptive filters for CDMA multiuser detection." Facta universitatis - series: Electronics and Energetics 17, no. 1 (2004): 21–32. http://dx.doi.org/10.2298/fuee0401021e.

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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.

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11

Ye, Chen, Guan Gui, Shin-ya Matsushita, and Li Xu. "Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1119–26. http://dx.doi.org/10.20965/jaciii.2016.p1119.

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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 “block&ell;0-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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12

Vázquez, Ángel A., Eduardo Pichardo, Juan G. Avalos, Giovanny Sánchez, Hugo M. Martínez, Juan C. Sánchez, and Héctor M. Pérez. "Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection." Applied Sciences 9, no. 21 (November 1, 2019): 4669. http://dx.doi.org/10.3390/app9214669.

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Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

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13

Horn, C., and R. H. Kwong. "Convergence of matrix LMS algorithms with applications to LMS/Newton." Signal Processing 44, no. 1 (June 1995): 103–15. http://dx.doi.org/10.1016/0165-1684(95)00018-9.

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14

Farooq, Umar, and Amalia Miliou. "Assessment of Different Channel Equalization Algorithms for a Converged OFDM-Based 5G mm-wave A-RoF System at 60 GHz." Applied Sciences 12, no. 3 (January 30, 2022): 1511. http://dx.doi.org/10.3390/app12031511.

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In this article, we simulate a converged 5G mm-wave analogue radio-over-fiber (A-RoF) system at 60 GHz, and perform offline signal processing to equalize the dispersive optical link with the three most frequently employed algorithms, i.e., the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA) and the adaptive median filtering (AMF), which are implemented in Matlab. The performances of the different algorithms are compared for various optical fiber lengths with respect to the EVM values obtained before and after equalization. In the case of QPSK in OFDM subcarriers, it is observed that the CMA algorithm performs better than the LMS and MF algorithms, with 2% and 1.4% EVM improvement respectively, while for 16QAM in OFDM subcarriers it is observed that the LMS algorithm has a very small improvement of 0.2% EVM compared to the MF algorithm, while CMA is not suitable for 16QAM modulation in the proposed converged 5G mm-wave A-RoF system at 60 GHz.

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15

Silfarney Alves Araújo, Rafael, Wemerson Delcio Parreira, and Renata Coelho Borges. "Análise Comparativa de Algoritmos Adaptativos Baseados em Least-Mean-Square para o Controle Ativo de Vibração." Anais do Computer on the Beach 14 (May 3, 2023): 303–10. http://dx.doi.org/10.14210/cotb.v14.p303-310.

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ABSTRACTAdaptive algorithms have been applied in noise and vibrationsuppression in several engineering areas. This work investigatesthe practicality of analysis between techniques of active vibrationcontrol for the suppression of movements arising from handtremors. The signals generated by the pathological motion havefrequencies between 3Hz and 6Hz, with the highest concentrationof energy in the gestures. These signals are acquired in thefundamental harmonic and the second harmonic, whereas physiologicaltremors vary between 2Hz and 12Hz. We have used computationaltools in Matlab to simulate these signals. We have usedthe least mean square (LMS) based algorithms, namely Filtered-xLeast Mean Square (Fx-LMS), Filtered-x Normalized Least MeanSquare (Fx-NLMS), and a hybrid Fx-NLMS&LMS. In our results, weidentify a faster response by the Fx-LMS&LMS algorithm in theactive vibration control for physiological tremors. In this example,the algorithm has required 3000 samples for steady-state.

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16

Chen, Guo Jun, and Han Ying Hu. "New Blind Adaptive Channel Estimation Schemes Based on OFDM Systems." Advanced Materials Research 709 (June 2013): 370–73. http://dx.doi.org/10.4028/www.scientific.net/amr.709.370.

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Two new blind estimation schemes based on decorrelation LMS（DLMS）and time-domain orthogonal LMS（TDO-LMS） algorithms are proposed to solve the problem of convergence in the estimation scheme based on LMS algorithm in wireless mobile communication OFDM systems. These schemes are improved on the traditional LMS algorithm.The factor of TDO-LMS algorithm changes along with the power of signal ,and the decorrelation LMS removes the correlation of the signal.Two methods can get faster convergence and better estimation performance combining with alterable step in the channel estimation of OFDM systems.Simulation shows that the convergence of the schemes proposed is better than that in the channel estimation based on the traditional LMS algorithm obviously.

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17

Yulin, Zhang, and Zhao Xiuyang. "Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System." Shock and Vibration 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/968082.

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To address the limitation of conventional adaptive algorithm used for active noise control (ANC) system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE), which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS) and Decomposition and Reconstruction LMS algorithm (DR-LMS) based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

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18

Sawant, Vishal V., and Mahesh Chavan. "Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters." International Journal of Computers and Communications 15 (April 14, 2021): 8–13. http://dx.doi.org/10.46300/91013.2021.15.2.

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Adaptive signal processing sensor arrays, known also as smart antennas .The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. The Least Mean Square (LMS) algorithm, is an adaptive algorithm .LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm for different parameters is analysed in this paper. This algorithm can be applied to beam forming with the software Matlab. The result obtain can achieve faster convergence and lower steady state error. The algorithms can be simulated in MATLAB 7.10 version.

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19

Xu, Fangmin, Chenyang Zheng, and Haiyan Cao. "Memory Distributed LMS for Wireless Sensor Networks." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9831378.

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Due to the limited communication resource and power, it is usually infeasible for sensor networks to gather data to a central processing node. Distributed algorithms are an efficient way to resolve this problem. In the algorithms, each sensor node deals with its own input data and transmits the local results to its neighbors. Each node fuses the information from neighbors and its own to get the final results. Different from the existing work, in this paper, we present an approach for distributed parameter estimation in wireless sensor networks based on the use of memory. The proposed approach consists of modifying the cost function by adding extra statistical information. A distributed least-mean squares (d-LMS) algorithm, called memory d-LMS, is then derived based on the cost function and analyzed. The theoretical performances of mean and mean square are analyzed. Moreover, simulation results show that the proposed algorithm outperforms the traditional d-LMS algorithm in terms of convergence rate and mean square error (MSE) performance.

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20

Zerai, A. A., and J. A. Bucklew. "Failure time analysis for LMS algorithms." IEEE Transactions on Information Theory 48, no. 4 (April 2002): 918–32. http://dx.doi.org/10.1109/18.992781.

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21

Choi, Y. S., H. C. Shin, and W. J. Song. "Robust Regularization for Normalized LMS Algorithms." IEEE Transactions on Circuits and Systems II: Express Briefs 53, no. 8 (August 2006): 627–31. http://dx.doi.org/10.1109/tcsii.2006.877280.

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22

Douglas, S. C. "A family of normalized LMS algorithms." IEEE Signal Processing Letters 1, no. 3 (March 1994): 49–51. http://dx.doi.org/10.1109/97.295321.

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23

Wang, Yanyan, and Yingsong Li. "Sparse Multipath Channel Estimation Using Norm Combination Constrained Set-Membership NLMS Algorithms." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8140702.

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A norm combination penalized set-membership NLMS algorithm with l0 and l1 independently constrained, which is denoted as l0 and l1 independently constrained set-membership (SM) NLMS (L0L1SM-NLMS) algorithm, is presented for sparse adaptive multipath channel estimations. The L0L1SM-NLMS algorithm with fast convergence and small estimation error is implemented by independently exerting penalties on the channel coefficients via controlling the large group and small group channel coefficients which are implemented by l0 and l1 norm constraints, respectively. Additionally, a further improved L0L1SM-NLMS algorithm denoted as reweighted L0L1SM-NLMS (RL0L1SM-NLMS) algorithm is presented via integrating a reweighting factor into our L0L1SM-NLMS algorithm to properly adjust the zero-attracting capabilities. Our developed RL0L1SM-NLMS algorithm provides a better estimation behavior than the presented L0L1SM-NLMS algorithm for implementing an estimation on sparse channels. The estimation performance of the L0L1SM-NLMS and RL0L1SM-NLMS algorithms is obtained for estimating sparse channels. The achieved simulation results show that our L0L1SM- and RL0L1SM-NLMS algorithms are superior to the traditional LMS, NLMS, SM-NLMS, ZA-LMS, RZA-LMS, and ZA-, RZA-, ZASM-, and RZASM-NLMS algorithms in terms of the convergence speed and steady-state performance.

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24

Tong, Guo Xiang, Fang Fang Lv, and Jian Tan. "Research of Noise Reduction Algorithm Based on Real Time." Applied Mechanics and Materials 532 (February 2014): 157–61. http://dx.doi.org/10.4028/www.scientific.net/amm.532.157.

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This Paper Mainly Summarized the Filtering Effect of Modified LMS Algorithms. Modified LMS Algorithms could Deal with the Noisy Speech at Real Time by Using the Correlated Property of Useful Signal and Uncorrelated Property of Noise to Strengthen the Relevant Part and Weaken the Irrelevant Part. the Modified Algorithms could be Compared by the SNR (Signal Noise Ratio) of Output Signal, MSE (Mean Square Error) and Misalignment with the Help of MATLAB Simulation so that the Advantages and Disadvantages of each Algorithm can be Summed up. Finally, in Order to get Faster Processing Speed, a New Idea that Combined the Modified LMS Algorithms with RNS (Residue Number System) was Proposed. the Combined System could Process the Signal in Parallel Instead of Serial so that Greatly Improved the Processing Speed and Guaranteed the Effectiveness of Real Time Processing.

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25

Praneeth, CH N. V. S., Jaba Deva Krupa Abel, Dhanalakshmi Samiappan, R. Kumar, S. Pravin Kumar, and Patnala Venkat Nitin. "A COMPARISON ON VARIANTS OF LMS USED IN FIR ADAPTIVE NOISE CANCELLERS FOR FETAL ECG EXTRACTION." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (July 29, 2020): 2050026. http://dx.doi.org/10.4015/s101623722050026x.

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Fetal electrocardiogram (FECG) non-invasively obtained through abdominal recordings serves as a promising diagnostic tool for fetal health monitoring during pregnancy. However, in the abdominal ECG (AECG) signal, FECG overlaps with maternal ECG (MECG) in both temporal and spectral domains in addition to interference from various sources like electromyogram, electrogastrogram, motion artifacts and other noises. The objective of this paper is to eliminate MECG components from AECG signal to extract FECG signal through FIR adaptive noise canceller (ANC) with filter coefficients updated using adaptive algorithms. Adaptive filters are suitable for current problem of interest and Least Mean Square (LMS) and its variants are analyzed for the problem of FECG extraction. We have compared the four variants of LMS such as normalized LMS (NLMS), sign-error algorithm, least mean fourth (LMF) algorithms for FECG extraction. The algorithms are evaluated using real-time abdominal ECG recordings acquired from daisy database. The performance of each algorithm is evaluated using various parameters like sensitivity, accuracy, positive predictive values and [Formula: see text] score. Further, the convergence rate for different algorithms are plotted and analyzed. From the simulation results, it is observed that the LMF algorithm outperforms its counterparts by providing an accuracy and positive predictive value of 73.3%, sensitivity of 100% and [Formula: see text] measure of 84.5%. The convergence plots obtained justify that LMF algorithm has a faster convergence rate compared to the other variants of LMS.

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26

Tajdari, Teimour. "Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms." Facta universitatis - series: Electronics and Energetics 34, no. 1 (2021): 133–40. http://dx.doi.org/10.2298/fuee2101133t.

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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.

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27

Panigrahi, T., P. M. Pradhan, G. Panda, and B. Mulgrew. "Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network." Journal of Computer Networks and Communications 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/601287.

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In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.

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28

Hassan, Ahmad Kamal, and Adnan Affandi. "On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA." Modelling and Simulation in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9742483.

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This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. Our contribution in this work is twofold. We initially provide a tutorial-like exposition for the design aspects of MSA and for the analytical framework of the two algorithms while our second aim is to take advantage of high nonlinearity of MSA to compare the effectiveness of LMS and that of RLS algorithms. We investigate the two algorithms by using gradient decent optimization in the context of radial basis function (RBF) of ANN. The proposed analysis is based on both static and adaptive spread factor. We model the forward side or synthesis of MSA by means of worked examples and simulations. Contour plots, 3D depictions, and Tableau presentations provide a comprehensive comparison of the two algorithms. Our findings point to higher accuracies in approximation for synthesis of MSA using RLS algorithm as compared with that of LMS approach; however the computational complexity increases in the former case.

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29

Jamel, Thamer M., and Faez Fawzi Hammood. "Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm." Journal of Engineering 24, no. 5 (May 1, 2018): 66. http://dx.doi.org/10.31026/j.eng.2018.05.05.

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In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

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30

Rupp, M. "Saving complexity of modified filtered-X-LMS and delayed update LMS algorithms." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 1 (1997): 57–60. http://dx.doi.org/10.1109/82.559371.

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31

Slock, D. T. M. "On the convergence behavior of the LMS and the normalized LMS algorithms." IEEE Transactions on Signal Processing 41, no. 9 (1993): 2811–25. http://dx.doi.org/10.1109/78.236504.

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32

Kim, Seon-Ho, Sang Bae Chon, Jun-Seok Lim, and Koeng-Mo Sung. "Novel LMS algorithms based on status categorization." IEICE Electronics Express 6, no. 18 (2009): 1361–67. http://dx.doi.org/10.1587/elex.6.1361.

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33

Mikhael, W., and A. Spanias. "Comparison of several frequency-domain LMS algorithms." IEEE Transactions on Circuits and Systems 34, no. 5 (May 1987): 586–88. http://dx.doi.org/10.1109/tcs.1987.1086164.

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34

Haweel, T. I., and P. M. Clarkson. "A class of order statistic LMS algorithms." IEEE Transactions on Signal Processing 40, no. 1 (1992): 44–53. http://dx.doi.org/10.1109/78.157180.

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35

Feuer, A., and R. Middleton. "Conditioning of LMS algorithms with fast sampling." IEEE Transactions on Signal Processing 43, no. 8 (1995): 1978–81. http://dx.doi.org/10.1109/78.403356.

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36

Davis, R. M. "Phase-only LMS and perturbation adaptive algorithms." IEEE Transactions on Aerospace and Electronic Systems 34, no. 1 (1998): 169–78. http://dx.doi.org/10.1109/7.640274.

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37

Diniz, P. S. R., and S. Werner. "Set-membership binormalized data-reusing LMS algorithms." IEEE Transactions on Signal Processing 51, no. 1 (2003): 124–34. http://dx.doi.org/10.1109/tsp.2002.806562.

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38

Diniz, P. S. R., and S. Werner. "Set-membership binormalized data-reusing lms algorithms." IEEE Transactions on Signal Processing 51, no. 1 (January 2003): 124–34. http://dx.doi.org/10.1109/tsp.2003.1145712.

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39

Augustyn, J. "Some LMS-based algorithms for impedance measurements." Measurement 41, no. 2 (February 2008): 178–85. http://dx.doi.org/10.1016/j.measurement.2006.07.011.

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40

Krstajić, Božo, LJubiša Stanković, and Zdravko Uskoković. "Combined Adaptive Filter with LMS-Based Algorithms." AEU - International Journal of Electronics and Communications 57, no. 4 (January 2003): 295–99. http://dx.doi.org/10.1078/1434-8411-54100175.

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41

Araújo, Rafael Silfarney Alves, Jéssica Cristina Tironi, Wemerson Delcio Parreira, Renata Coelho Borges, Juan Francisco De Paz Santana, and Valderi Reis Quietinho Leithardt. "Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression Control." Applied Sciences 13, no. 5 (March 2, 2023): 3199. http://dx.doi.org/10.3390/app13053199.

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The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

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42

Radhi Shabib Kaned. "Investigation of Phase Noise on the Performance of LMS-RLS Adaptive Equalizer." Diyala Journal of Engineering Sciences 6, no. 1 (March 1, 2013): 27–35. http://dx.doi.org/10.24237/djes.2013.06103.

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This paper investigates the effect of phase noise on equalization of communication channels using least mean square (LMS) and recursive least square (RLS) adaptive algorithms. The aim of the investigation is to mitigate inter-symbol interference (ISI) caused by the channel and to impose the bit error rate (BER) in the received signals. The equalizerusestwobasicadaptivealgorithms: LMS algorithmand RLS algorithm. Without LMS-RLS equalizer,theBER ismorethan when the system modelincludesLMS-RLS equalizer as indicated in table (1) and table (2). Equalizer algorithm is analyzed using MATLAB v.9 Communication Block Set.

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43

Wang, Da Ming, Jian Hui Wang, Wei Jia Cui, and Xu Hui Yang. "An Adaptive Narrow-Band Filter with Variable Step." Applied Mechanics and Materials 644-650 (September 2014): 4103–6. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4103.

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LMS algorithm is a kind of classic adaptive algorithms. Although it has the virtue of simple operation, it also shows the defects of relatively slow convergence and big steady state errors in low SNR. To remedy these defects, this paper put forward a new variable steps adaptive LMS algorithm. In the transient state, the learning rate increases slowly with the iteration times which accelerate the convergence rate of LMS algorithm. In the steady state, the learning rate decreases gradually with the iteration times which guarantee the convergence accuracy of LMS algorithm. After this improved algorithm is applied in the design of adaptive wavetrap, the simulation results show that it can not only effectively ease up the conflicts between convergence rates and steady state errors, but also improve the performance of wavetrap in real-time trapping.

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44

MURUGAN, S. SAKTHIVEL, V. NATARAJAN, and S. RADHA. "ANALYSIS OF MNLMS AND KLMS ALGORITHM FOR UNDERWATER ACOUSTIC COMMUNICATIONS." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250023. http://dx.doi.org/10.1142/s021947751250023x.

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The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance measure characteristics such as signal to noise ratio (SNR) and mean square error (MSE). The MNLMS is developed by calculating an optimum learning parameter that best suits for the acoustic signal used. The analysis is carried out for a range of 100 Hz to 10 KHz source signals and the algorithm proves that any ambient noise signals against the source signal in this range can be eliminated and the source signal can be reconstructed. Our simulation results show that KLMS and MNLMS algorithms achieve remarkable performance even in the very low SNR region as compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for MNLMS and KLMS.

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45

Bismor, Dariusz, and Marek Pawelczyk. "Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis." Archives of Acoustics 41, no. 4 (December 1, 2016): 731–39. http://dx.doi.org/10.1515/aoa-2016-0070.

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AbstractThe Least Mean Square (LMS) algorithm and its variants are currently the most frequently used adaptation algorithms; therefore, it is desirable to understand them thoroughly from both theoretical and practical points of view. One of the main aspects studied in the literature is the influence of the step size on stability or convergence of LMS-based algorithms. Different publications provide different stability upper bounds, but a lower bound is always set to zero. However, they are mostly based on statistical analysis. In this paper we show, by means of control theoretic analysis confirmed by simulations, that for the leaky LMS algorithm, a small negative step size is allowed. Moreover, the control theoretic approach alows to minimize the number of assumptions necessary to prove the new condition. Thus, although a positive step size is fully justified for practical applications since it reduces the mean-square error, knowledge about an allowed small negative step size is important from a cognitive point of view.

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46

Bharti, Manisha. "Performance Enhancement of Coherent Optical OFDM System Using LMS Algorithm." Journal of Telecommunications and Information Technology 1 (March 31, 2020): 1–5. http://dx.doi.org/10.26636/jtit.2020.137919.

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Instability of the local oscillator causes phase noise – a phenomenon that is a disadvantage and is considered to be a major obstacle in the functioning of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. An attempt has been made in this paper to reduce the eﬀects of common phase errors generated by phase noise. In this paper, a least mean square (LMS) based algorithm is proposed for estimation of phase noise. Using this proposed algorithm, the major problem of phase ambiguity caused by cycle slip is avoided and the bit error rate is greatly improved. Further, there is no requirement for modifying the frame structure of OFDM using this algorithm. A CO-OFDM system with the 8-PSK technique is used to implement the algorithm concerned. Furthermore, the algorithm, using the 8-PSK modulation technique, is analyzed and compared with the existing QPSK technique and with other algorithms. The investigations reveal that 8-PSK outperforms existing LMS algorithms using other techniques and signiﬁcantly reduces the bit error rate.

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47

Atzemourt, Mossaab, Abdelmajid Farchi, Younes Chihab, and Zakaria Hachkar. "Performance Evaluation of LMS and CM Algorithms for Beamforming." Advances in Materials Science and Engineering 2022 (March 23, 2022): 1–6. http://dx.doi.org/10.1155/2022/7744625.

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In this paper, we compare the performances of the least mean square (LMS) and constant modulus (CM) algorithms for beamforming. Our interest in these algorithms finds its origins in their reliability as a source-receiver pair. In addition, their use brings a great frequency of diversity even to respond quickly to the increasing spectral demand. The results suggest that the greater the number of elements in the antenna, the better the directivity for both LMS and CM. We also note that a judicious choice of the control parameter mu leads to a better speed of convergence for the two algorithms. Let us note, however, that LMS is more efficient. Our simulations show that in an environment affected by white Gaussian noise, LMS is more robust than CM. This confirms the theoretical result due to the fact that LMS uses a sequence for learning. Performance analyses of the two techniques are simulated in the MATLAB environment.

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48

Yan, Peng Cheng, Meng Ran Zhou, and Yun Chuan Ma. "Compared of Polarity Correlation Algorithm and LMS Algorithm for Flow Velocity Measurement." Advanced Materials Research 945-949 (June 2014): 2155–59. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2155.

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This paper summarizes the basic principle of flow velocity measurement, analysis of the structure of the polarity correlation algorithm, the circuit implementation, and the peak point determination, then introduced the LMS(Least mean square) algorithm in flow velocity measurement , thereafter,the simulation experiment of the two algorithms are performed. Experimental results show that with a noisy environment, the measurement accuracy of the polarity correlation algorithm reduced, since the LMS algorithm can automatically adjust its parameters to the external factors, so it can still have a accurate date in flow velocity measurement.

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49

M. Tabra, Yasmine, and Bayan Sabbar. "Hybrid MVDR-LMS beamforming for Massive MIMO." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 715. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp715-723.

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<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>

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50

Husaeni, Dwi Novia Al, and Daris Hadianto. "The Influence of Spada Learning Management System (LMS) on Algorithm Learning and Programming of First Grade Students at Universitas Pendidikan Indonesia." Indonesian Journal of Multidiciplinary Research 2, no. 1 (September 4, 2021): 203–12. http://dx.doi.org/10.17509/ijomr.v2i1.42906.

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This study discusses the effect of using a Learning Management System (LMS) on learning Algorithms and Programming. This study aims to determine the effectiveness of the LMS Spada in the Algorithm and Programming learning process, to determine the positive and negative impacts of using LMS, and to find out how to improve the learning process to make it more effective. The method in this study is a survey method with 32 participants. The data was obtained from distributing questionnaires through WhatsApps social media. The results of this study, it is known that LMS is very effective in learning Algorithms and Programming. Then in this study it was also stated that the use of LMS in the learning process had a positive impact and a negative impact. In addition to knowing the effectiveness, positive and negative impacts, in this study we can find ways to improve the learning process to be more effective, including: Continue to practice and read, be brave, take advantage of existing learning media, complete facilities and infrastructure and others.

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