Journal articles: 'Algorithme LMS (Least Mean Square)'

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Ibrahim Khan, Muhammad, Muhammad Juanid Mughal, and Rana Liaqat Ali. "Cosine Least Mean Square Algorithm for Adaptive Beamforming." International Journal of Engineering & Technology 7, no. 3.16 (July 26, 2018): 94. http://dx.doi.org/10.14419/ijet.v7i3.16.16191.

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Beamforming and multiple-input multiple-output (MIMO) antenna configurations have received worldwide interest during the recent time. Various beamforming algorithm has been proposed and employed in different applications. The Least Mean Square (LMS) algorithm has become one of the most widespread adaptive beamforming techniques because of its simplicity and robustness. This paper presents a new variant of LMS algorithm named as Cosine Least Mean Square (Cos-LMS) which uses the efficient computation of array factor for linear antenna array.This algorithm gives improved performance in beam width reduction, side lobe level reduction, null depth, and stability as compared to standard LMS and other variants of LMS algorithm. The performance improvement by Cos-LMS algorithm is accomplished without increasing the computationalcomplexity of standard LMS algorithm.

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Rahman, Aviv Yuniar, Mamba’us Sa’adah, and Istiadi. "Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (April 19, 2020): 286–95. http://dx.doi.org/10.29207/resti.v4i2.1667.

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Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.

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Tanpreeyachaya, Jirasak, Ichi Takumi, and Masayasu Hata. "A New Partial-normalized Least Mean Square Algorithm." IEEJ Transactions on Electronics, Information and Systems 116, no. 1 (1996): 57–65. http://dx.doi.org/10.1541/ieejeiss1987.116.1_57.

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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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Javed, Shazia, and Noor Atinah Ahmad. "A Stochastic Total Least Squares Solution of Adaptive Filtering Problem." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/625280.

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An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.

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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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Martinek, Radek, Jaroslav Rzidky, Rene Jaros, Petr Bilik, and Martina Ladrova. "Least Mean Squares and Recursive Least Squares Algorithms for Total Harmonic Distortion Reduction Using Shunt Active Power Filter Control." Energies 12, no. 8 (April 24, 2019): 1545. http://dx.doi.org/10.3390/en12081545.

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This paper deals with the use of least mean squares (LMS, NLMS) and recursive least squares (RLS) algorithms for total harmonic distortion (THD) reduction using shunt active power filter (SAPF) control. The article presents a pilot study necessary for the construction of our own controlled adaptive modular inverter. The objective of the study is to find an optimal algorithm for the implementation. The introduction contains a survey of the literature and summarizes contemporary methods. According to this research, only adaptive filtration fulfills our requirements (adaptability, real-time processing, etc.). The primary benefit of the paper is the study of the efficiency of two basic approaches to adaptation ((N)LMS and RLS) in the application area of SAPF control. The study examines the impact of parameter settings (filter length, convergence constant, forgetting factor) on THD, signal-to-noise ratio (SNR), root mean square error (RMSE), percentage root mean square difference (PRD), speed, and stability. The experiments are realized with real current and voltage recordings (consumer electronics such as PC source without power factor correction (PFC), HI-FI amplifier, etc.), which contain fast dynamic transient phenomena. The realized model takes into account a delay caused by digital signal processing (DSP) (the implementation of algorithms on field programmable gate array (FPGA), approximately 1–5 μs) and a delay caused by the reaction time of the proper inverter (approximately 100 μs). The pilot study clearly showed that the RLS algorithm is the most suitable for the implementation of an adaptive modular inverter because it achieved the best results for all analyzed parameters.

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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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PavanKalyan, I., G. Jaya Santosh, K. H. K. Prasad, and Durgesh Nandan. "Study of Echo Cancellation approach by using Least Mean Square (LMS) Algorithm." Journal of Physics: Conference Series 1714 (January 2021): 012053. http://dx.doi.org/10.1088/1742-6596/1714/1/012053.

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Fang, Yubin, Xiaojin Zhu, Zhiyuan Gao, Jiaming Hu, and Jian Wu. "New feedforward filtered-x least mean square algorithm with variable step size for active vibration control." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 1 (November 14, 2018): 187–98. http://dx.doi.org/10.1177/1461348418812326.

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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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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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Alhaj, H. M. M., N. M. Nor, Vijanth S. Asirvadam, M. F. Abdullah, and T. Ibrahim. "Estimation of Power System Harmonic Using Modified Normalized Least Mean Square." Applied Mechanics and Materials 785 (August 2015): 378–82. http://dx.doi.org/10.4028/www.scientific.net/amm.785.378.

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A new adaptive power system harmonic estimator is presented, which is competent of tracking power system harmonic components. The proposed estimator technique is based on the normalized Least Mean Square (LMS), which is a stochastic gradient descent algorithm. The learning method of the proposed estimator is based upon the recursive estimate of the signal power, and is faster tracking of harmonic components as compared to the introduced Adaptive Linear Element (ADALINE).

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COMINGUEZ, A. H. "A NEW GENERALIZED LEAST MEAN-SQUARE ALGORITHM FOR PROCESSING NON-STATIONARY SEISMIC DATA." Geofísica Internacional 26, no. 3 (July 1, 1987): 393–406. http://dx.doi.org/10.22201/igeof.00167169p.1987.26.3.1312.

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Se presenta un algoritmo adaptable apropiado para deconvolver trazas, el cual está basado sobre una expresión generalizada de la técnica de mínimo error cuadrático medio. El uso del nuevo proceso se recomienda especialmente para elaborar sismogramas de reflexión sísmica que contengan reverberaciones variables en el tiempo. Mediante la aplicación del sistema adaptable los coeficientes del operador se recalculaban para cada tiempo de la señal de entrada. Tanto las características de convergencia del algoritmo como sus propiedades de estabilidad se analizan y comparan con las del algoritmo tradicional LMS. Para tal efecto se presentan ilustraciones con sismogramas sintéticos. La aplicabilidad del método expuesto parece promisoria para pruebas sísmicas en agues proco profundas.

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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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He, Hong, Cong Cong Wu, Tong Yang, Lin He, and Dan Li. "Analysis of Smart Antenna Interference Suppression Base on LMS Improved Algorithm." Key Engineering Materials 474-476 (April 2011): 1019–23. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1019.

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Smart antenna technology can increase channel capacity, improve spectrum efficiency and enlarge cover area by using its spatial diversity ability , which greatly improve system performance . A least mean squares (LMS) is posed for the smart antenna adaptive interference suppression system based on the training sequence. Also , the least mean square (LMS) and least squares (RLS) algorithm are proposed for the design and simulation about interference suppression and compare and analyze the result which can prove the effectiveness about algorithm in TD-SCDMA system .According to the results, the new method with a faster convergence speed, which doesn`t matter with interference environments, is better than LMS.

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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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Sano, Hisashi, Shuichi Adachi, and Hideki Kasuya. "Application of a Least Squares Lattice Algorithm to Active Noise Control for an Automobile." Journal of Dynamic Systems, Measurement, and Control 119, no. 2 (June 1, 1997): 318–20. http://dx.doi.org/10.1115/1.2801256.

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The purpose of this paper is to propose an alternative approach to active noise control (ANC) using the least squares lattice (LSL) algorithm. Typically, in ANC applications, the least-mean-square (LMS) algorithm has been used because of its simplicity. However, the LMS algorithm has the disadvantage of slow convergence speed in the case of broadband noise, such as the road noise present in the passenger compartment of automobiles traveling on rough road surfaces. In order to solve this problem, the LSL algorithm for ANC is considered. By computer simulation using actual car data, the LSL algorithm proves to be more effective than the LMS one.

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Reddy, Praveen, and Dr Baswaraj Gadgay. "FPGA based least mean square algorithm for noise cancellation in communication system." International Journal of Engineering & Technology 7, no. 3.3 (May 31, 2018): 165. http://dx.doi.org/10.14419/ijet.v7i2.32.15588.

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We present modified Distributed Arithmetic (DA) based architecture for LMS Adaptive filter which has improved the throughput of the filter also area and power has been comparatively been reduced. As we know, the adaptive filter uses continuous recalculation and generation of new coefficients will generate the negative effect on the use of algorithm. We have used a special temporary LUT addressing technique has overcome the issues resulting in better performance and good results. In this paper, we have discussed about the adaptive filter and implementation of DA adaptive filter and also discussed the results obtained from the design. Comparison with traditional de-sign has also been done to show the effectiveness of the algorithm.

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Sulthana, Asiya, and Md Zia Ur Rahman. "Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System." International Journal of Engineering & Technology 7, no. 2.17 (April 15, 2018): 74. http://dx.doi.org/10.14419/ijet.v7i2.17.11562.

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An increasing number of elderly and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.

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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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Yasin, Muhammad, and Muhammad Junaid Hussain. "A Novel Adaptive Algorithm Addresses Potential Problems of Blind Algorithm." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5983924.

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A hybrid algorithm called constant modulus least mean square (CMLMS) algorithm is proposed in order to address the potential problems existing with constant modulus algorithm (CMA) about its convergence. It is a two-stage adaptive filtering algorithm and based on least mean square (LMS) algorithm followed by CMA. A hybrid algorithm is theoretically developed and the same is verified through MatLab Software. Theoretical model is verified through simulation and its performance is evaluated in smart antenna in presence of a cochannel interfering signal and additive white Gaussian noise (AWGN) of zero mean. This is also tested in Rayleigh fading channel using digital modulation technique for Bit Error Rate (BER). Finally, a few computer simulations are presented in order to substantiate the theoretical findings with respect to proposed model. Corresponding results obtained with the use of only CMA and LMS algorithms are also presented for further comparison.

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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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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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Abdalla, Ahmed, Suhad Mohammed, Abdelazeim Abdalla, Tang Bin, and Mohammed Ramadan. "A Study of a various Acoustic Beamforming Techniques Using a Microphone Array." Journal of Communications Technology, Electronics and Computer Science 1 (October 22, 2015): 7. http://dx.doi.org/10.22385/jctecs.v1i0.3.

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In this paper, A study of numerous acoustic beamforming algorithms is carried out. Beamforming algorithms are techniques utilize to determine the Direction of arrival of (DOA) the speech signals while suppress out the corresponding noises and interferences. The simple delay and sum beamformer technique which use the constrained least mean squares (LMS) filter for spatial filtering is firstly investigated. Secondly, a constrained least mean square algorithm (also known as Frost Beamformer) is considered. The beamformer algorithms are simulated in MATLAB and therefore, the simulation results indicate that there a significant enhancement in the Signal-to-Noise-Ratio (SNR) for frost beamformer as compared to the simple delay and sum beamformer.

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N. Jayanthi, P., and S. Ravishankar. "Model-based compressed sensing algorithms for MIMO- OFDM channel estimation." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 5. http://dx.doi.org/10.14419/ijet.v7i2.4.10030.

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High data rates on the wireless channel can be achieved by combining orthogonal frequency division multiplexing (OFDM) with multiple input multiple output (MIMO) communication modulation scheme. MIMO-OFDM system impulse response of the channel is approximately sparse. Sparse channelestimation can be done using Compressive Sensing (CS) techniques. In this paper, a low complexity model based CoSaMp Compressive Sensing (CS) algorithm with conventional tools namely Least Square (LS) and Least Mean Square (LMS) are used for MIMO-OFDM channel estimation. Simulation results show amodel based CoSaMP for MIMO-OFDM channel estimation with LMS tool the Normalized Mean Square Error(NMSE)reduced by 34%with very reduced complexity.

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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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Prasetyowati, Sri Arttini Dwi, and Adhi Susanto. "Multiple Processes for Least Mean Square Adaptive Algorithm on Roadway Noise Cancelling." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (April 1, 2015): 355. http://dx.doi.org/10.11591/ijece.v5i2.pp355-360.

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Noise is a problem often found in daily life. Noise also make people could not concentrate to do their work. Efforts to reduce noise have been proposed, but, due to variety of the noise’s characteristics, every noise problem requires different solution. This research aim to cancel the vehicle’s noise while maintaining the information heard. These conditions happened in the hospitals classrooms, or work room near the roadway. The vehicle’s noise change very fast, so the adaptive system is the good solution candidate for solving this problem. On the beginning, the simulation process had the trouble with the iterations. Matlab software only can execute the certain range of iteration. It could not cancel the noise, even the information becomes criptic. The problem is how to cancell the vehicle’s noise with the restriction software and still manage the important information. This research will modify the LMS adaptive algorithm so that the iteration could be done by the system and the main goal of the research could be reached. The modification of the algorithm is based on the filter length (L) used to adapt with the noise. Therefore, this research conducted simulation of the Adaptive Noise Cancelling with two process steps. The output of the first adaptive process have the.same characteristics with the noise that would be cancelled, thus the first adaptive process have the error near to zero. The second adaptive process changes the input by the output of the first process and mix the information into the noise. Error occured in the final process is the information heard as the dominant output.

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Shakeel, Aqsa, Toshihisa Tanaka, and Keiichi Kitajo. "Time-Series Prediction of the Oscillatory Phase of EEG Signals Using the Least Mean Square Algorithm-Based AR Model." Applied Sciences 10, no. 10 (May 23, 2020): 3616. http://dx.doi.org/10.3390/app10103616.

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Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.

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Salman, Mohammad Shukri, Alaa Eleyan, and Bahaa Al-Sheikh. "Discrete wavelet transform-based RI adaptive algorithm for system identification." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2383. http://dx.doi.org/10.11591/ijece.v10i3.pp2383-2391.

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In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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Yu, Zhihua, Yunfei Cai, and Daili Mo. "Comparative Study on Noise Reduction Effect of Fiber Optic Hydrophone Based on LMS and NLMS Algorithm." Sensors 20, no. 1 (January 5, 2020): 301. http://dx.doi.org/10.3390/s20010301.

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Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the reference channel, the sensing signal of the interferometric fiber-optic hydrophone is denoised by two adaptive filtering schemes based on the least mean square (LMS) algorithm and the normalized least mean square (NLMS) algorithm respectively. The results show that the LMS algorithm is superior to the NLMS algorithm in reducing total harmonic distortion, improving the signal-to-noise ratio and filtering effect.

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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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Suresh, D., G. Sravanthi, and R. Chander. "DSTATCOM with Improved LMS based IRP theory." E3S Web of Conferences 87 (2019): 01013. http://dx.doi.org/10.1051/e3sconf/20198701013.

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In this paper, grid connected DSTATCOM with three phase three wire has been proposed for harmonics elimination, reactive power compensation and active power injection into the grid. An improved instantaneous reactive power theory based on the least mean square is computed. The least mean square (LMS) algorithm is combined with instantaneous reactive power theory (IRPT) to improve its dynamics performance and eliminates the use of low pass filter requirement for computation of reference current. The harmonics component of the active power is estimated from LMS algorithm generated load harmonics currents. The performance characteristic of DSTATCOM is estimated with control scheme using computer simulation. The technique is based on the instantaneous transformation of instantaneous signals and taking advantage of the subsequent calculation of power from supply side to the loads. The neural network based method to estimate the harmonic online, DSTATCOM can compensate the harmonics. The computer simulation is carried out with MATLAB Simulink power system block sets.

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S, Siva Priyanka, and Kishore Kumar T. "Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement." Traitement du Signal 38, no. 3 (June 30, 2021): 785–95. http://dx.doi.org/10.18280/ts.380325.

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In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

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Chung, I., and S. Ann. "On the asymptotic analysis of the smoothed least mean square algorithm and the relation with other LMS-type algorithms." IEEE Transactions on Circuits and Systems 38, no. 12 (1991): 1551–54. http://dx.doi.org/10.1109/31.108509.

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Sivashanmugam, Radhika, and Sivabalan Arumugam. "Robust Adaptive Algorithm by an Adaptive Zero Attractor Controller of ZA-LMS Algorithm." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3945895.

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This paper proposes a new approach to identify time varying sparse systems. The proposed approach uses Zero-Attracting Least Mean Square (ZA-LMS) algorithm with an adaptive optimal zero attractor controller which can adapt dynamically to the sparseness level and provide appreciable performance in all environments ranging from sparse to nonsparse conditions. The optimal zero attractor controller is derived based on the criterion that confirms largest decrease in mean square deviation (MSD) error. A simple update rule is also proposed to change the zero attractor controller based on the level of sparsity. It is found that, for nonsparse system, the proposed approach converges to LMS (as ZA-LMS cannot outperform LMS when the system is nonsparse) and, for highly sparse system, as the proposed approach is based on optimal zero attractor controller, it converges either similar to ZA-LMS or even better than ZA-LMS (depending on the value of zero attractor controller chosen for ZA-LMS algorithm). The performance of the proposed algorithm is better than ZA-LMS and LMS when the system is semisparse. Simulations were performed to prove that the proposed algorithm is robust against variable sparsity level.

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Pan, Hong Xia, and Ying Ying Zhang. "Study and Improving on Adaptive Filter." Applied Mechanics and Materials 121-126 (October 2011): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1392.

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In this paper the principle of adaptive filter and various least mean square (LMS) adaptive filter algorithm is studied, based on the related hyperbolic tangent function LMS algorithm is presented, referred to as CTanh-LMS algorithm. Simulation results show that, compared with other adaptive filter algorithm, this method has better denoising ability, and the algorithm is simple, fast convergence rate, and can satisfy the gearbox vibration signal denoising requirements. The proposed algorithm can not only solve the gearbox fault feature extraction, and give adaptive filter algorithm research provides a new means, has important theoretical significance and practical value.

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Wang, Fengsui, Qisheng Wang, Furong Liu, Jingang Chen, Linjun Fu, and Fa Zhao. "Improved NLMS-based adaptive denoising method for ECG signals." Technology and Health Care 29, no. 2 (March 12, 2021): 305–16. http://dx.doi.org/10.3233/thc-202659.

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BACKGROUND: Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm. OBJECTIVE: In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS). METHODS: The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology. RESULTS: Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325. CONCLUSION: The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.

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Ebrahimzadeh, Elias, Mohammad Pooyan, Sahar Jahani, Ahmad Bijar, and Seyed Kamal Setaredan. "ECG SIGNALS NOISE REMOVAL: SELECTION AND OPTIMIZATION OF THE BEST ADAPTIVE FILTERING ALGORITHM BASED ON VARIOUS ALGORITHMS COMPARISON." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (August 2015): 1550038. http://dx.doi.org/10.4015/s1016237215500386.

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The electrocardiogram (ECG) is generally used for the diagnosis of cardiovascular diseases. In many of the biomedical applications, it is necessary to remove the noise from ECG recordings. Several adaptive filter structures have been proposed for noise cancellation. Compared to the least mean square (LMS) method, the unbiased and normalized adaptive noise reduction (UNANR) algorithm has better performance, as mentioned in previous investigations. In this paper, we review various kinds of ECG noise reduction algorithms. To provide a detailed and fair comparison, all normalized LMS (NLMS), Block LMS (BLMS), recursive least squares (RLS) and UNANR algorithms are implemented and their performance have been assessed using the same dataset and compared to different state-of-the-art approaches. Then, the performance analysis of all five algorithms is presented and compared in term of mean squared error (MSE), computational complexity and stability. The obtained results revealed that RLS method is much more effective and powerful than other methods in ECG noise cancellation, and even better than UNANR. Then, in order to reach the best performance of the mentioned filter and also, to minimize the output signal error, the optimized parameters of the algorithm were defined and results were investigated. The obtained outcomes show that the best Lambda (λ) occurs between 0.05 and 0.9, so that the convergence rate of the optimized RLS filter is faster than others. It not only decreases the noise, but also the ECG waveform is better conserved. Furthermore, the introduced optimized method with adaptive threshold value would have great potential in biomedical application of signal processing and other fields.

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Silva, André, Marco Gomes, João P. Vilela, and Willie K. Harrison. "SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security." Sensors 21, no. 3 (January 28, 2021): 856. http://dx.doi.org/10.3390/s21030856.

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In order to secure wireless communications, we consider the usage of physical-layer security (PLS) mechanisms (i.e., coding for secrecy mechanisms) combined with self-interference generation. We present a prototype implementation of a scrambled coding for secrecy mechanisms with interference generation by the legitimate receiver and the cancellation of the effect of self-interference (SI). Regarding the SI cancellation, four state-of-the-art algorithms were considered: Least mean square (LMS), normalized least mean square (NLMS), recursive least squares (RLS) and QR decomposition recursive least squares (QRDRLS). The prototype implementation is performed in real-world software-defined radio (SDR) devices using GNU-Radio, showing that the LMS outperforms all other algorithms considered (NLMS, RLS and QRDRLS), being the best choice to use in this situation (SI cancellation). It was also shown that it is possible to secure communication using only noise generation by the legitimate receiver, though a variation of the packet loss rate (PLR) and the bit error rate (BER) gaps is observed when moving from the fairest to an advantageous or a disadvantageous scenario. Finally, when noise generation was combined with the adapted scrambled coding for secrecy with a hidden key scheme, a noteworthy security improvement was observed resulting in an increased BER for Eve with minor interference to Bob.

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Partap, Bhanu, Prabhjot Singh, and . "Hybrid Approach for Channel Estimation Using Iterative Compensation and LMS Algorithm." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 34. http://dx.doi.org/10.14419/ijet.v7i3.8.15214.

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In Orthogonal Frequency Division Multiplexing, the concept of channel equalization and estimation plays a vital role to improve the performance of the system by reducing the effects of distortion in the signals that occurs due to fading, multipath, delay spreads. In this study a hybrid channel estimation technique is developed by collaborating the iterative compensation mechanism with Least Mean Square technique. The performance of the proposed work is observed to be more effective and efficient than the Iterative compensation based channel estimation technique in the terms of Mean Square Error (MSE).

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Yasin, M., and Pervez Akhtar. "Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 952–61. http://dx.doi.org/10.1108/compel-07-2014-0184.

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Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

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He, Hong, Cong Cong Wu, Hang Li, Tong Yang, Lin He, and Ying He. "Smart Antenna Adaptive Interference Suppression in TD-SCDMA System." Advanced Materials Research 204-210 (February 2011): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.476.

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The Smart Antenna which can adaptively track the user signals plays an important role in reducing the interferences among users in TD-SCDMA. This paper focuses on the study of the smart antenna adaptive beam-forming algorithm and aims at realizing the design and emulation of it by using the least mean square algorithm (LMS) and the recursive least squares algorithm (RLS). And by the comparison and analysis of the result, we want to learn its effectiveness in reducing the interferences in TD-SCDMA system.

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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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Yan, Dong, Xian Zhou, Li Min Xia, Xiao Yun Wu, and Na Na Zhang. "A LMS-Based Equalization Algorithm for 28Gbaud PM-MQAM Optical Coherent System." Advanced Materials Research 760-762 (September 2013): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.268.

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In this paper, we presented an improved equalization algorithm which use Least Mean Square algorithm (LMS) training-based as pre-convergence, after reaching the threshold, equalization switches to directed decision LMS to compensate a various of impairs and decrease bit error rate (BER) to achieve better performance in the steady-state, the experiment is based on a 28GbaudMQAM polarization multiplexing optical coherent system.

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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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Rai, Amrita, and A. K. Kohli. "Simulation and Analysis of Nonlinear System Identification Using the LMS Volterra Filter." Advanced Materials Research 403-408 (November 2011): 3528–37. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3528.

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Nonlinear system Identification based on Volterra filter are widely used for the nonlinearity identification in various application. A standard algorithm for LMS-Volterra filter for system identification simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Simulations performed under MATLAB show remarkable differences between convergence criteria with various value of the step size. Along with that the least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. The second order LMS Volterra filter with variable step size for system identification are analyzed and comparing the theoretical value with experimental value. Copyright © 2009 IFSA.

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Dan, Feng, Fan Shangchun, and Zheng Dezhi. "A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter." Transactions of the Institute of Measurement and Control 40, no. 1 (June 29, 2016): 261–68. http://dx.doi.org/10.1177/0142331216652955.

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In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.

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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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Wang, Yun Liang, and Guo Cun Li. "Simulation and Comparison of Adaptive Detection Algorithm." Applied Mechanics and Materials 130-134 (October 2011): 2692–95. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2692.

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The active power filter has two key link for harmonic current detection and respectively compensation current tracking. This article mainly aims at harmonic current detection link. Firstly, Analysis adaptive harmonic detection methods, then based on over to LMS (Least Mean Square) adaptive algorithm as the research object, in discussing the adaptive algorithm criteria. By comparing several simulation-based LMS algorithm to achieve the results of the filter,analyse the causes of the results.

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Aali, Seyed Reza, Mohammad Reza Besmi, and Mohammad Hosein Kazemi. "Smart VRP-NLMS algorithm for estimation of power system frequency." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 1 (January 7, 2019): 362–81. http://dx.doi.org/10.1108/compel-06-2018-0263.

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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 ± Î² 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.

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Journal articles on the topic 'Algorithme LMS (Least Mean Square)'

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