Relevant bibliographies by topics / Least mean squares (LMS)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Least mean squares (LMS)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Least mean squares (LMS)"

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Lapointe, Marcel, Huu Tue Huynh, and Paul Fortier. "Highly parallel architecture for the least mean squares (LMS) algorithm." Canadian Journal of Electrical and Computer Engineering 16, no. 3 (1991): 93–104. http://dx.doi.org/10.1109/cjece.1991.6592939.

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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 (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 autocorrelatio
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Kwon, Bokyu, and Soohee Han. "Least-Mean-Square Receding Horizon Estimation." Mathematical Problems in Engineering 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/631759.

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We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation. The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon. Anya prioristate information is not required, and existing artificial constraints for easy derivation are not imposed. For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form. For numerical reliability, the iterative form is prese
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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
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Pratiwi, Nor Kumalasari Caecar, Rita Magdalena, Yunendah Nur Fuadah, Sofia Saidah, Syamsul Rizal, and Muhamad Rokhmat Isnaini. "Denoising Sinyal EEG dengan Algoritma Recursive Least Square dan Least Mean Square." TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol 5, no. 2 (2019): 122–29. http://dx.doi.org/10.15575/telka.v5n2.122-129.

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EEG mengukur fluktuasi tegangan yang dihasilkan dari arus ionik yang beredar sepanjang neuron otak. Dalam pengaturan eksperimental, sinyal EEG sering terkontaminasi dengan berbagai noise akibat gerakan otot dan jantung. Noise dengan magnitudo yang lebih tinggi dari sinyal aslinya akan merusak sinyal EEG dan bisa berakibat fatal dalam analisis diagnosa. Sehingga diperlukan sebuah sistem denoising yang mampu secara maksimal mengurangi noise, tanpa menghilangkan komponen informasi penting dari sinyal EEG. Salah satu algoritma yang dapat digunakan dalam mereduksi noise pada sinyal biomedis adalah
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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 (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 proce
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Hossain, Md Moswer, Md Motiur Rahman, and Md Masud Rana. "Least Mean Square (LMS) for Smart Antenna." Universal Journal of Communications and Network 1, no. 1 (2013): 16–21. http://dx.doi.org/10.13189/ujcn.2013.010103.

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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
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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 (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
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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 tha
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Dissertations / Theses on the topic "Least mean squares (LMS)"

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Wang, Dongmei. "Least mean square algorithm implementation using the texas instrument digital signal processing board." Ohio University / OhioLINK, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1175279376.

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Yapici, Yavuz. "A Bidirectional Lms Algorithm For Estimation Of Fast Time-varying Channels." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613220/index.pdf.

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Effort to estimate unknown time-varying channels as a part of high-speed mobile communication systems is of interest especially for next-generation wireless systems. The high computational complexity of the optimal Wiener estimator usually makes its use impractical in fast time-varying channels. As a powerful candidate, the adaptive least mean squares (LMS) algorithm offers a computationally efficient solution with its simple first-order weight-vector update equation. However, the performance of the LMS algorithm deteriorates in time-varying channels as a result of the eigenvalue disparity, i.
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Bajic, Vladan. "DESIGN AND IMPLEMENTATION OF AN ADAPTIVE NOISE CANCELING SYSTEM IN WAVELET TRANSFORM DOMAIN." University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1132784671.

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Kim, Taeho, and Monika Ivantysynova. "Active Vibration Control of Axial Piston Machine using Higher Harmonic Least Mean Square Control of Swash Plate." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-199412.

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Noise emission is a major drawback of the positive displacement machine. The noise source can be divided into structure borne noise source (SBNS) and fluid borne noise source (FBNS). Passive techniques such as valve plate optimization have been used for noise reduction of axial piston machines. However, passive techniques are only effective for limited operating conditions or at least need compromises in design. In this paper, active vibration control of swash plate is investigated for vibration and noise reduction over a wide range of operating conditions as an additional method to passive no
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Lovstedt, Stephan P. "Improving Performance of the Filtered-X Least Mean Square Algorithm for Active Control of Noise Contatining Multiple Quasi-Stationary Tones." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2290.pdf.

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Callahan, Michael J. "Estimating Channel Identification Quality in Passive Radar Using LMS Algorithms." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1503508289044109.

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Gao, Wei. "Kernel LMS à noyau gaussien : conception, analyse et applications à divers contextes." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4076/document.

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L’objectif principal de cette thèse est de décliner et d’analyser l’algorithme kernel-LMS à noyau Gaussien dans trois cadres différents: celui des noyaux uniques et multiples, à valeurs réelles et à valeurs complexes, dans un contexte d’apprentissage distributé et coopératif dans les réseaux de capteurs. Plus précisement, ce travail s’intéresse à l’analyse du comportement en moyenne et en erreur quadratique de cas différents types d’algorithmes LMS à noyau. Les modèles analytiques de convergence obtenus sont validés par des simulations numérique. Tout d’abord, nous introduiso
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Gribaudo, Michael Louis. "Development of a system model and least mean square (LMS) filter for the Naval Postgraduate School (NPS) Infrared Search and Target Designation (IRSTD) system." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/26990.

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Tajany, Mostafa. "Égalisation adaptative de multitrajets dans des liaisons de télémesure à haut débit." Nantes, 1996. http://www.theses.fr/1996NANT2002.

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Dernier-né des services de télédétection, le système commercial SPOT conçu par le Centre National des Études Spatiales (CNES) et opérationnel depuis 1986, transmet vers le sol les informations images numérisées. Pour diverses configurations du satellite, la qualité de la liaison est fortement dégradée par la présence de multi-trajet, dû aux interactions entre l'antenne d'émission et la structure du satellite. Les travaux présentés dans ce mémoire s'inscrivent dans le cadre de l'étude de l'adaptativité d'antenne bord et sol intervenant dans les liaisons de télémesure de charge utile (TMCU) à ha
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Deyneka, Alexander. "Metody ekvalizace v digitálních komunikačních systémech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218963.

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Tato práce je psaná v angličtině a je zaměřená na problematiku ekvalizace v digitálních komunikačních systémech. Teoretická část zahrnuje stručné pozorování různých způsobů návrhu ekvalizérů. Praktická část se zabývá implementací nejčastěji používaných ekvalizérů a s jejich adaptačními algoritmy. Cílem praktické části je porovnat jejich charakteristiky a odhalit činitele, které ovlivňují kvalitu ekvalizace. V rámci problematiky ekvalizace jsou prozkoumány tři typy ekvalizérů. Lineární ekvalizér, ekvalizér se zpětnou vazbou a ML (Maximum likelihood) ekvalizér. Každý ekvalizér byl testován na mo
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Books on the topic "Least mean squares (LMS)"

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Gribaudo, Michael Louis. Development of a system model and least mean square (LMS) filter for the Naval Postgraduate School (NPS) Infrared Search and Target Designation (IRSTD) system. Naval Postgraduate School, 1989.

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Sentana, Enrique. Least squares predictions and mean-variance analysis. London School of Economics, Financial Markets Group, 1999.

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name, No. Least-mean-square adaptive filters. Wiley-Interscience, 2003.

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Adaptive filtering: Fundamentals of least mean squares with MATLAB. CRC Press/Taylor & Francis Group, 2014.

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Macchi, Odile. Adaptive processing: The least mean squares approach with applications in transmission. John Wiley & Sons, 1995.

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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 1. Least-squares Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0018.

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This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental desig
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Wagner, Kevin, and Milos Doroslovacki. Proportionate-Type Normalized Least Mean Square Algorithms. Wiley & Sons, Incorporated, John, 2013.

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Wagner, Kevin, and Milos Doroslovacki. Proportionate-Type Normalized Least Mean Square Algorithms. Wiley & Sons, Incorporated, John, 2013.

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1929-, Widrow Bernard, and Haykin Simon S. 1931-, eds. Least-mean-square adaptive filters. John Wiley, 2003.

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Performance characteristics of an adaptive controller based on least-mean-square filters. National Aeronautics and Space Administration, Ames Research Center, 1987.

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Book chapters on the topic "Least mean squares (LMS)"

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Alexander, S. Thomas. "The Least Mean Squares (LMS) Algorithm." In Adaptive Signal Processing. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_5.

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Hassibi, Babak. "On the Robustness of LMS Filters." In Least-Mean-Square Adaptive Filters. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch4.

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Hänsler, Eberhard, and Gerhard Uwe Schmidt. "Control of LMS-Type Adaptive Filters." In Least-Mean-Square Adaptive Filters. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch6.

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Butterweck, Hans J. "Traveling-Wave Model of Long LMS Filters." In Least-Mean-Square Adaptive Filters. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch2.

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Wagner, Kevin, and Miloš Doroslovački. "LMS Analysis Techniques." In Proportionate-Type Normalized Least Mean Square Algorithms. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579558.ch2.

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Ramirez, Paulo Sergio. "The Least-Mean-Square (LMS) Algorithm." In The Kluwer International Series in Engineering and Computer Science. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3637-3_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_3.

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Diniz, Paulo Sergio Ramirez. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_3.

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Conference papers on the topic "Least mean squares (LMS)"

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Minardi II, John E., and Michael J. Minardi. "SAR Images Generated Using the Least Mean Squares (LMS) Algorithm." In Defense and Security Symposium, edited by Edmund G. Zelnio and Frederick D. Garber. SPIE, 2007. http://dx.doi.org/10.1117/12.731458.

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Wang, Wei, Chuankun Mu, Hongru Song, and Miao Yu. "Improved Adaptive Convex Combination of Least Mean Square (LMS) Algorithm." In 2010 International Conference on Computational and Information Sciences (ICCIS). IEEE, 2010. http://dx.doi.org/10.1109/iccis.2010.145.

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Zuo, Lei, and Samir A. Nayfeh. "Adaptive Least-Mean Square Feed-Forward Control With Actuator Saturation by Direct Minimization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85494.

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The least-mean squares (LMS) adaptive feedforward algorithm is used widely for vibration and noise cancellation. If reference signals become large enough to saturate that actuators, the filter coefficients in such algorithms can diverge. The leaky LMS method limits the controller effort by augmenting the objective function by a weighted control effort, and is known to attain good performance and avoid growth of filter coefficients for well-chosen weights. We propose an algorithm that seeks to directly minimize the mean-square cost in the presence of saturation. We derive the true stochastic gr
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Gupta, Saurav, Ajit Kumar Sahoo, and Upendra Kumar Sahoo. "Parameter estimation of Wiener nonlinear model using least mean square (LMS) algorithm." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8228077.

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Wies, R. W., and J. W. Pierre. "Use of least-mean squares (LMS) adaptive filtering technique for estimating low-frequency electromechanical modes in power systems." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1025429.

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Budihal, Suneeta V., and R. M. Banakar. "Performance Analysis of Adaptive Decision Feedback Turbo Equalization (ADFTE) Using Recursive Least Square (RLS) Algorithm over Least Mean Square (LMS) Algorithm." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.81.

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Mansoor, Umair bin, and Syed Muhammad Asad. "A Robust, Iteration Dependent Variable Step-Size (RID-VSS) Least-Mean Square (LMS) Adaptive Algorithm." In 2020 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2020. http://dx.doi.org/10.1109/iceet48479.2020.9048197.

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Ram, M. Raghu, K. Venu Madhav, E. Hari Krishna, K. Nagarjuna Reddy, and K. Ashoka Reddy. "On the performance of Time Varying Step-size Least Mean Squares(TVS-LMS) adaptive filter for MA reduction from PPG signals." In 2011 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2011. http://dx.doi.org/10.1109/iccsp.2011.5739353.

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Castillo, Karl Vincent G., Neil Kenneth T. Mendoza, Chelsea Andrea S. Morales, Allen Dominic A. Perez, Jansen Yna L. Unisa, and Angelo R. dela Cruz. "Denoising of Spatiotemporal Gait Waveforms from Motion-Sensing Depth Camera using Least Mean Square (LMS) Adaptive Filter." In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 2018. http://dx.doi.org/10.1109/hnicem.2018.8666294.

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Smith, Bradley R., and H. H. Robertshaw. "Adaptive LMS Model-Following Control Applied Inside a Closed-Loop." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-3783.

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Abstract A Least Mean Squares (LMS)-style algorithm is derived for the feedback control problem. The algorithm allows a tap delay line within the closed loop to be used for control applications. This paper derives the algorithm and applies the algorithm to two simple control problems for which the solution is known and to one problem with an unknown solution. The first problem is a stable second-order system. The second problem is a unstable second-order system which is initially stabilized with the feedback loop. In both problems, the weights converge to the expected values. The stable proble
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Reports on the topic "Least mean squares (LMS)"

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Bershad. Statistical Analysis of the LMS (Last Mean Squares) and Modified Stochastic Gradient. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada195497.

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