Relevant bibliographies by topics / Least mean square (LMS) algorithm

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Academic literature on the topic 'Least mean square (LMS) algorithm'

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Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Least mean square (LMS) algorithm"

1

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 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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Mishra, Swastika, and Jibendu Roy. "Sparse echo cancellation using variants of least mean fourth and least mean square algorithms." Facta universitatis - series: Electronics and Energetics 36, no. 4 (2023): 519–32. http://dx.doi.org/10.2298/fuee2304519m.

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Echo cancellation is the most essential and indispensable component of telephone networks. The impulse responses of most of the networks are sparse in nature; that is, the impulse response has a small percentage of its components with a significant magnitude (large energy), while the rest are zero or small. In these sparse environments, conventional adaptive algorithms like least mean square (LMS) and normalized LMS (NLMS) show substandard and inferior performances. In this paper, the performances of the normalized least mean square (NLMS) algorithm, the normalized least mean fourth (NLMF) and the proportionate normalized least mean fourth (PNLMF) are compared for sparse echo cancellation. The sparseness of both the echo response and the input signal is exploited in this algorithm to achieve improved results at a low computational cost. The PNLMF algorithm showed better results and faster convergence in sparse and non sparse systems, but its results in sparse environments are more impressive. The NLMF algorithm shows good results in sparse environments but not in non-sparse environments. The PNLMS algorithm can be considered superior to the NLMF and NLMS algorithms with respect to the error profile. A modified algorithm, the sparse controlled modified proportionate normalized LMF (SCMPNLMF) algorithm, is proposed, and its performances are compared with the other algorithms.
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Sandu, Ajay Kumar. "Design and Implementation of Least Mean Square Adaptive Filter Using Verilog." International Journal of Innovative Science and Modern Engineering (IJISME) 12, no. 12 (2024): 1–7. https://doi.org/10.35940/ijisme.C4566.12121224.

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<strong>Abstract: </strong>Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn&rsquo;t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.
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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 (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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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 (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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Kumar, Sandu Ajay, and Dr T. Satya Savithri. "Design and Implementation of Least Mean Square Adaptive Filter Using Verilog." International Journal of Innovative Science and Modern Engineering 12, no. 12 (2024): 1–7. https://doi.org/10.35940/ijisme.c4566.12121224.

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Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn’t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.
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Chander, Ramavath, Edara Venkata Chandra Sekhara Rao, and Erukula Vidyasagar. "Least mean square based adaptive control of active power filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 2 (2024): 1072–80. https://doi.org/10.11591/ijpeds.v15.i2.pp1072-1080.

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The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.
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Lee, Han-Sol, Changgyun Jin, Chanwoo Shin, and Seong-Eun Kim. "Sparse Diffusion Least Mean-Square Algorithm with Hard Thresholding over Networks." Mathematics 11, no. 22 (2023): 4638. http://dx.doi.org/10.3390/math11224638.

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This paper proposes a distributed estimation technique utilizing the diffusion least mean-square (LMS) algorithm, specifically designed for sparse systems in which many coefficients of the system are zeros. To efficiently utilize the sparse representation of the system and achieve a promising performance, we have incorporated L0-norm regularization into the diffusion LMS algorithm. This integration is accomplished by employing hard thresholding through a variable splitting method into the update equation. The efficacy of our approach is validated by comprehensive theoretical analysis, rigorously examining the mean stability as well as the transient and steady-state behaviors of the proposed algorithm. The proposed algorithm preserves the behavior of large coefficients and strongly enforces smaller coefficients toward zero through the relaxation of L0-norm regularization. Experimental results show that the proposed algorithm achieves superior convergence performance compared with conventional sparse algorithms.
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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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Dissertations / Theses on the topic "Least mean square (LMS) algorithm"

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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.e., spread, of the input correlation matrix in such chan nels. In this work, we incorporate the L MS algorithm into the well-known bidirectional processing idea to produce an extension called the bidirectional LMS. This algorithm is shown to be robust to the adverse effects of time-varying channels such as large eigenvalue spread. The associated tracking performance is observed to be very close to that of the optimal Wiener filter in many cases and the bidirectional LMS algorithm is therefore referred to as near-optimal. The computational complexity is observed to increase by the bidirectional employment of the LMS algorithm, but nevertheless is significantly lower than that of the optimal Wiener filter. The tracking behavior of the bidirectional LMS algorithm is also analyzed and eventually a steady-state step-size dependent mean square error (MSE) expression is derived for single antenna flat-fading channels with various correlation properties. The aforementioned analysis is then generalized to include single-antenna frequency-selective channels where the so-called ind ependence assumption is no more applicable due to the channel memory at hand, and then to multi-antenna flat-fading channels. The optimal selection of the step-size values is also presented using the results of the MSE analysis. The numerical evaluations show a very good match between the theoretical and the experimental results under various scenarios. The tracking analysis of the bidirectional LMS algorithm is believed to be novel in the sense that although there are several works in the literature on the bidirectional estimation, none of them provides a theoretical analysis on the underlying estimators. An iterative channel estimation scheme is also presented as a more realistic application for each of the estimation algorithms and the channel models under consideration. As a result, the bidirectional LMS algorithm is observed to be very successful for this real-life application with its increased but still practical level of complexity, the near-optimal tracking performa nce and robustness to the imperfect initialization.
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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 introduisons l’algorithme LMS, les espaces de Hilbert à noyau reproduisants, ainsi que les algorithmes de filtrage adaptatif à noyau existants. Puis, nous étudions analytiquement le comportement de l’algorithme LMS à noyau Gaussien dans le cas où les statistiques des éléments du dictionnaire ne répondent que partiellement aux statistiques des données d’entrée. Nous introduisons ensuite un algorithme LMS modifié à noyau basé sur une approche proximale. La stabilité de l’algorithme est également discutée. Ensuite, nous introduisons deux types d’algorithmes LMS à noyaux multiples. Nous nous concentrons en particulier sur l’analyse de convergence de l’un d’eux. Plus généralement, les caractéristiques des deux algorithmes LMS à noyaux multiples sont analysées théoriquement et confirmées par les simulations. L’algorithme LMS à noyau complexe augmenté est présenté et ses performances analysées. Enfin, nous proposons des stratégies de diffusion fonctionnelles dans les espaces de Hilbert à noyau reproduisant. La stabilité́ de cas de l’algorithme est étudiée<br>The main objective of this thesis is to derive and analyze the Gaussian kernel least-mean-square (LMS) algorithm within three frameworks involving single and multiple kernels, real-valued and complex-valued, non-cooperative and cooperative distributed learning over networks. This work focuses on the stochastic behavior analysis of these kernel LMS algorithms in the mean and mean-square error sense. All the analyses are validated by numerical simulations. First, we review the basic LMS algorithm, reproducing kernel Hilbert space (RKHS), framework and state-of-the-art kernel adaptive filtering algorithms. Then, we study the convergence behavior of the Gaussian kernel LMS in the case where the statistics of the elements of the so-called dictionary only partially match the statistics of the input data. We introduced a modified kernel LMS algorithm based on forward-backward splitting to deal with $\ell_1$-norm regularization. The stability of the proposed algorithm is then discussed. After a review of two families of multikernel LMS algorithms, we focus on the convergence behavior of the multiple-input multikernel LMS algorithm. More generally, the characteristics of multikernel LMS algorithms are analyzed theoretically and confirmed by simulation results. Next, the augmented complex kernel LMS algorithm is introduced based on the framework of complex multikernel adaptive filtering. Then, we analyze the convergence behavior of algorithm in the mean-square error sense. Finally, in order to cope with the distributed estimation problems over networks, we derive functional diffusion strategies in RKHS. The stability of the algorithm in the mean sense is analyzed
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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 modelu, který simuloval reálnou přenosovou soustavu s komplexním zkreslením, která je složena z útlumu, mezisymbolové interference a aditivního šumu. Na základě implenentace byli určeny charakteristiky ekvalizérů a stanoveno že optimální výkon má ML ekvalizér. Adaptační algoritmy hrají významnou roli ve výkonnosti všech zmíněných ekvalizérů. V práci je nastudována skupina stochastických algoritmů jako algoritmus nejmenších čtverců(LMS), Normalizovaný LMS, Variable step-size LMS a algoritmus RLS jako zástupce deterministického přístupu. Bylo zjištěno, že RLS konverguje mnohem rychleji, než algoritmy založené na LMS. Byly nastudovány činitele, které ovlivnili výkon popisovaných algoritmů. Jedním z důležitých činitelů, který ovlivňuje rychlost konvergence a stabilitu algoritmů LMS je parametr velikosti kroku. Dalším velmi důležitým faktorem je výběr trénovací sekvence. Bylo zjištěno, že velkou nevýhodou algoritmů založených na LMS v porovnání s RLS algoritmy je, že kvalita ekvalizace je velmi závislá na spektrální výkonové hustotě a a trénovací sekvenci.
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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 haut débit. Dans un premier temps, les conséquences du multi-trajet ont été quantifiées en terme d'interférence intersymboles et de taux d'erreurs sur les bits. On a déterminé la rejection nécessaire sur les trajets retardés pour approcher les performances nominales. Divers traitements au sol ont par la suite été envisagés sous forme d'un égaliseur. Une étude théorique des performances d'un égaliseur, menée sur une structure linéaire et sur une structure à décision dans la boucle (DFE), a permis de prédire les performances en taux d'erreurs et en erreur quadratique moyenne. Les résultats obtenus sont ceux de l'algorithme global et sont complétés par un test des autres principaux algorithmes adaptatifs. De plus, un ensemble de traitements a été simulé sur une chaine idéale et sur deux liaisons de TMCU. L'algorithme du gradient stochastique LMS a été testé sur un modèle de canal le plus probable. Les performances des structures d'égaliseur ont été élaborées sous une forme comparative. En vue d'une implantation pratique, la structure à décision dans la boucle fournit des meilleures performances avec deux coefficients dans la branche linéaire et deux coefficients dans la branche récursive DFE (2,2). Un traitement à bord du satellite a été proposé. Il consiste à adapter le diagramme de rayonnement de l'antenne active après son optimisation suivant un certain critère d'adaptation. Sur le plan matériel, en raison du grand débit numérique de transmission (50 mbit/s) de la liaison TMCU, les implémentations classiques d'un égaliseur ne peuvent pas être utilisées. Deux solutions de réalisation matérielle ont été proposées: la première est une solution hybride mêlant les techniques analogiques pour le filtrage et une approche numérique pour le calcul des coefficients. La seconde est totalement analogique. L'algorithme proposé est l'algorithme LMS intégré au démodulateur cohérent à quatre états de phase (mdp4). Cette approche a été simulée avec une structure DFE (2,2). Une étude de sensibilité aux défauts de réalisation a permis d'évaluer de façon plus approfondie la faisabilité de ces implémentations
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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.

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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Cavalcanti, Bruno Jácome. "Análise de modelos de predição de perdas de propagação em redes de comunicações LTE e LTE-Advanced usando técnicas de inteligência artificial." PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/25061.

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Submitted by Automação e Estatística (sst@bczm.ufrn.br) on 2018-04-11T20:06:38Z No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5)<br>Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-04-16T20:46:19Z (GMT) No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5)<br>Made available in DSpace on 2018-04-16T20:46:19Z (GMT). No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5) Previous issue date: 2017-10-20<br>A perfeita funcionalidade dos sistemas de comunicações de 3ª. e 4ª. gerações requerem, entre outras coisas, do conhecimento dos valores numéricos da predição das perdas de propagação dos sinais propagantes nos ambientes urbano, suburbano e rural. Portanto, o estudo das condições de propagação em um ambiente qualquer sempre será uma preocupação dos engenheiros projetistas. A análise e desenvolvimento de modelos robustos de predição de perdas de propagação em redes de comunicações Long Term Evolution (LTE) e Long Term Evolution Advanced (LTE-A) usando técnicas de Inteligência Artificial são realizadas neste trabalho. Os procedimentos metodológicos empregados foram aplicados no melhoramento da predição dos modelos de perda de propagação empíricos SUI, ECC-33, Ericsson 9999, TR 36.942 e o modelo do Espaço Livre, aplicados em redes LTE e LTE-A nas frequências de 800 MHz, 1800 MHz e 2600 MHz, para ambientes suburbanos em cidades de porte médio do nordeste do Brasil. Assim, nesta tese propõem-se dois modelos de Redes Neurais Artificiais (RNA): (i) o modelo de rede neural com entradas baseadas em erro (RNBE), utilizando como principal alimentador da rede o erro entre dados medidos e simulados, e, (ii) o modelo de rede neural com entradas baseadas no terreno (RNBT). O desempenho desses modelos foram comparados com os modelos de propagação considerados no trabalho e também as versões otimizadas utilizando Algoritmos Genéticos (AG) e o Método dos Mínimos Quadrados (LMS). Também foram realizadas comparações com valores medidos, obtidos a partir de uma campanha de medição realizada na cidade de Natal, Estado do Rio Grande do Norte. Os resultados finais obtidos através de simulações e medições apresentaram boas concordâncias métricas, com destaque para a performance do modelo RNBE. A principal contribuição dessa tese é que, ao utilizar essas técnicas que fazem uso de maneira mais eficiente dos modelos de propagação empíricos, pode-se estimar sinais propagantes realistas, evitando erros no planejamento e implementações de redes sem fio LTE e LTE-A em áreas suburbanas.<br>The perfect functionality of the 3rd and 4th generation of wireless systems requires, among other parameters, knowledge of the numerical values of the prediction of loss of propagation of propagation signals in urban, suburban and rural environments. Therefore, the study of propagation conditions in any environment will always be a concern of design engineers. The analysis and development of robust propagation loss prediction models in Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A) communications networks using Artificial Intelligence techniques is performed in this work. The methodologies used were applied to improve the prediction of loss of empirical propagation SUI, ECC-33, Ericsson 9999, TR 36.942 models and the Free Space model applied in LTE and LTE-A networks in the frequencies of 800 MHz, 1800 MHz and 2600 MHz, for suburban environments in mid-sized cities in northeastern Brazil. Thus, in these thesis two models of Artificial Neural Networks (RNA) are proposed: (i) the neural network model with inputs based on error (RNBE) using as main feeder of the network the error between measured and simulated data, and (ii) the neural network model with land-based inputs (RNBT). The performance of these models was compared with the models of propagation considered in the work and also the versions optimized using Genetic Algorithms (AG) and the Least Square Method (LMS). Comparisons were also made with measured values, obtained from a measurement campaign carried out in the city of Natal, state of Rio Grande do Norte. The final results obtained through simulations and measurements presented good metric concordances, with emphasis on the performance of the RNBE model. Thus, the main contribution of this thesis is that, by using these techniques that make more efficient use of empirical propagation models, we can estimate realistic propagation signals, avoiding errors in the planning and implementations of LTE and LTE- A wireless networks in suburban areas.
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Laichi, Farouk. "Performance study of the leaky least mean square adaptive algorithm with delayed adjustments." Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6748.

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Adaptive filters constitute an important part of signal processing. They are widely used in many applications where signal statistics are unknown to the users. One of the most popular adaptive methods is known as the least mean-squared (LMS) algorithm. It is well known for its hardware simplicity and its cost effectiveness. However, the LMS adaptive algorithm does not operate well in non-ideal environments. Leakage in the updated equation of the LMS algorithm was proven to overcome many such problems. This algorithm is known as the leaky LMS (LLMS). The LLMS is very robust to drifting phenomena and overflow in registers; problems that occur in the digital implementation of adaptive algorithms. In many applications, an inherent delay in the feedback error path is encountered. This delay has a definite effect on the performance of the LLMS algorithm. In this thesis, we study the performance of LLMS in the presence of delay (LDLMS). This algorithm is basically the LLMS with a delay incorporated in the coefficient updated equation. A new general stability bound is derived for the LDLMS, from which bounds of convergence of LMS, LLMS, and delayed LMS(DLMS) can be obtained. Stability bounds, convergence behavior, and excess mean squared error for this new algorithm are investigated. Theoretical results are first derived and later verified by simulations. It will be shown that introducing leakage in the DLMS algorithm gives a compromise performance. Finally, examples of applications of the LDLMS algorithm are provided.
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Books on the topic "Least mean square (LMS) algorithm"

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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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Book chapters on the topic "Least mean square (LMS) algorithm"

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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 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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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 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 US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_3.

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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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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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Ullah, Farooq Kifayat. "Evaluation of Dc/Dc Buck Converter Controlled by LMS (Least Mean Square) Algorithm for Different Values of Load Capacitor." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28962-0_50.

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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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Conference papers on the topic "Least mean square (LMS) algorithm"

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Bi, Chenchong, Yongchun Miao, Liping Li, and Yingsong Li. "Kernel Weibull M-transform Least Mean Square Algorithm." In 2024 IEEE 12th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2024. https://doi.org/10.1109/iccsnt62291.2024.10776707.

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Moinuddin, Muhammad, Azzedine Zerguine, Ubaid M. Al-Saggaf, and Muhammad Arif. "Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942885.

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Sitjongsataporn, Suchada. "Multitask Diffusion Adaptive Step-size Normalised Least Mean Square Algorithm." In 2024 International Conference on Power, Energy and Innovations (ICPEI). IEEE, 2024. http://dx.doi.org/10.1109/icpei61831.2024.10749040.

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Bukya, Mahipal, Bhukya Padma, Rajesh Kumar, and Akhilesh Mathur. "Insulation Detection of Electric Vehicle Using Least Mean Square Algorithm." In 2025 International Conference on Next Generation Communication & Information Processing (INCIP). IEEE, 2025. https://doi.org/10.1109/incip64058.2025.11019934.

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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 gradient of the cost for systems with saturation with respect to the filter coefficients and obtain an adaptation rule very close to that of the filtered-x algorithm, but in the proposed algorithm, the reference filter is a time-varying modification of the secondary channel. In simulations of an active vibration isolation system with actuator limits subject to random ground vibration, the leaky LMS algorithm attains its best performance with actuation weights small enough to allow significant actuator saturation but large enough to prevent divergence. The proposed algorithm attains performance better that attained by the leaky LMS algorithm, and does not require the selection of weights.
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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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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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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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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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