Relevant bibliographies by topics / RLS Algorithm

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Academic literature on the topic 'RLS Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 June 2025

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Journal articles on the topic "RLS Algorithm"

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Zorkun, Aral Ertug, Miguel A. Salas-Natera, and Ramón Martínez Rodríguez-Osorio. "An Improved Hybrid Beamforming Algorithm for Fast Target Tracking in Satellite and V2X Communication." Remote Sensing 16, no. 1 (2023): 13. http://dx.doi.org/10.3390/rs16010013.

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Autonomous remote sensing systems establish communication links between nodes. Ensuring coverage and seamless communication in highly dense environments is not a trivial task as localization, separation, and tracking of targets, as well as interference suppression, are challenging. Therefore, smart antenna systems fulfill these requirements by employing beamforming algorithms and are considered a key technology for autonomous remote sensing applications. Among many beamforming algorithms, the recursive least square (RLS) algorithm has proven superior convergence and convergence rate performances. However, the tracking performance of RLS degrades in the case of dynamic targets. The forgetting factor in RLS needs to be updated constantly for fast target tracking. Additionally, multiple beamforming algorithms can be combined to increase tracking performance. An improved hybrid constant modulus RLS beamforming algorithm with an adaptive forgetting factor and a variable regularization factor is proposed. The forgetting factor is updated using the low-complexity yet robust adaptive moment estimation method (ADAM). The sliding-window technique is applied to the proposed algorithm to mitigate the steady-state noise. The proposed algorithm is compared with existing RLS-based algorithms in terms of convergence, convergence rate, and computational complexity. Based on the results, the proposed algorithm has at least 10 times better convergence (accuracy) and a convergence rate two times faster than the compared RLS-based algorithms.
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Prongnuch, Sethakarn, Suchada Sitjongsataporn, and Theerayod Wiangtong. "Diffusion recursive least squares algorithm based on triangular decomposition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5101. http://dx.doi.org/10.11591/ijece.v13i5.pp5101-5108.

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<span lang="EN-US">In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.</span>
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Khan, Noor M., and Hasan Raza. "Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms." Wireless Communications and Mobile Computing 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/1248796.

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In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP) operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS) algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of 95.83% and 82.29% decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.
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Xu, X., H. He, and D. Hu. "Efficient Reinforcement Learning Using Recursive Least-Squares Methods." Journal of Artificial Intelligence Research 16 (April 1, 2002): 259–92. http://dx.doi.org/10.1613/jair.946.

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The recursive least-squares (RLS) algorithm is one of the most well-known algorithms used in adaptive filtering, system identification and adaptive control. Its popularity is mainly due to its fast convergence speed, which is considered to be optimal in practice. In this paper, RLS methods are used to solve reinforcement learning problems, where two new reinforcement learning algorithms using linear value function approximators are proposed and analyzed. The two algorithms are called RLS-TD(lambda) and Fast-AHC (Fast Adaptive Heuristic Critic), respectively. RLS-TD(lambda) can be viewed as the extension of RLS-TD(0) from lambda=0 to general lambda within interval [0,1], so it is a multi-step temporal-difference (TD) learning algorithm using RLS methods. The convergence with probability one and the limit of convergence of RLS-TD(lambda) are proved for ergodic Markov chains. Compared to the existing LS-TD(lambda) algorithm, RLS-TD(lambda) has advantages in computation and is more suitable for online learning. The effectiveness of RLS-TD(lambda) is analyzed and verified by learning prediction experiments of Markov chains with a wide range of parameter settings. The Fast-AHC algorithm is derived by applying the proposed RLS-TD(lambda) algorithm in the critic network of the adaptive heuristic critic method. Unlike conventional AHC algorithm, Fast-AHC makes use of RLS methods to improve the learning-prediction efficiency in the critic. Learning control experiments of the cart-pole balancing and the acrobot swing-up problems are conducted to compare the data efficiency of Fast-AHC with conventional AHC. From the experimental results, it is shown that the data efficiency of learning control can also be improved by using RLS methods in the learning-prediction process of the critic. The performance of Fast-AHC is also compared with that of the AHC method using LS-TD(lambda). Furthermore, it is demonstrated in the experiments that different initial values of the variance matrix in RLS-TD(lambda) are required to get better performance not only in learning prediction but also in learning control. The experimental results are analyzed based on the existing theoretical work on the transient phase of forgetting factor RLS methods.
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Kostromitsky, S. M., I. N. Davydzenko, and A. A. Dyatko. "Equivalent forms of writing of processing algorithms of adaptive antenna array." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 67, no. 2 (2022): 230–38. http://dx.doi.org/10.29235/1561-8358-2022-67-2-230-238.

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The article is devoted to obtaining equivalent forms of writing of processing algorithms for the operation of adaptive antenna arrays, considering algorithms as varieties of some generalized LMS algorithm. This will facilitate a comparative analysis of the algorithms’ characteristics. The following algorithms of operation are considered: LMS, NLMS, LMS-Newton, SMI, RLS. The article contains the initial operation algorithms of adaptive antenna arrays, conclusions of equivalent processing algorithms and an equivalent block diagram of the generalized LMS algorithm. Equivalent forms of writing the operation algorithms of adaptive antenna arrays and their parameters are also presented in tabular form. Of particular interest is the equivalent operation algorithm in the case of the SMI algorithm, which differs most from the LMS algorithm. Equivalent algorithms differ only by the scalar convergence coefficient and the matrix normalizing factor. For LMS-Newton, SMI, and RLS algorithms, the matrix normalizing factor is the same, it is determined by inverting the estimation of the correlation matrix of input signals and reduces the dependence of the characteristics of the algorithms on the parameters of the correlation matrix. The scalar convergence coefficient of equivalent algorithms in the case of SMI and RLS algorithms depends on the iteration number and tends to zero for the SMI algorithm and to some non-zero value for the RLS algorithm. The dependence of the convergence coefficient on the iteration number makes it possible to optimize the characteristics of the algorithms at the transition stage. The tendency of the convergence coefficient to zero in the case of the SMI algorithm makes it effective only for stationary input signals. The non-zero steady-state value of the convergence coefficient in the case of the RLS algorithm allows its effective use in a non-stationary environment.
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Skidmore, I. D., and I. K. Proudler. "The KaGE RLS algorithm." IEEE Transactions on Signal Processing 51, no. 12 (2003): 3094–104. http://dx.doi.org/10.1109/tsp.2003.818997.

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Hatun, Metin. "Identification of Wiener Systems with Recursive Gauss-Seidel Algorithm." Elektronika ir Elektrotechnika 29, no. 5 (2023): 4–10. http://dx.doi.org/10.5755/j02.eie.35119.

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The Recursive Gauss-Seidel (RGS) algorithm is presented that is implemented in a one-step Gauss-Seidel iteration for the identification of Wiener output error systems. The RGS algorithm has lower processing intensity than the popular Recursive Least Squares (RLS) algorithm due to its implementation using one-step Gauss-Seidel iteration in a sampling interval. The noise-free output samples in the data vector used for implementation of the RGS algorithm are estimated using an auxiliary model. Also, a stochastic convergence analysis is presented, and it is shown that the presented auxiliary model-based RGS algorithm gives unbiased parameter estimates even if the measurement noise is coloured. Finally, the effectiveness of the RGS algorithm is verified and compared with the equivalent RLS algorithm by computer simulations.
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Jaros, Rene, Radek Martinek, Radana Kahankova, et al. "Optimization of RLS Algorithm for Hybrid Method ICA-RLS." IFAC-PapersOnLine 52, no. 27 (2019): 530–35. http://dx.doi.org/10.1016/j.ifacol.2019.12.718.

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

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

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Brown, Andrew P., and Ronald A. Iltis. "DISTRIBUTED TERRESTRIAL RADIOLOCATION USING THE RLS ALGORITHM." International Foundation for Telemetering, 2002. http://hdl.handle.net/10150/605572.

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International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California<br>This paper presents the development of two distributed terrestrial radiolocation algorithms that use range estimates derived from DS-CDMA waveforms. The first algorithm, which is RLS-based, is derived as the solution of an approximate least-squares positioning problem. This algorithm has the advantage of reduced computational complexity, compared with the EKF-based algorithm that is presented. It is shown via simulations that both positioning algorithms perform well, with the performance of the EKF-based algorithm being superior.
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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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Nyberg, Tobias. "Torque-Based Load Estimation for Passenger Vehicles." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179208.

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An accurate estimate of the mass of a passenger vehicle is important for several safety systems and environmental aspects. In this thesis, an algorithm for estimating the mass of a passenger vehicle using the recursive least squares methodis presented. The algorithm is based on a physical model of the vehicle and is designed to be able to run in real-time onboard a vehicle and uses the wheel torque signal calculated in the electrical control unit in the engine. Therefore no estimation of the powertrain is needed. This is one contribution that distinguishes this thesis from previous work on the same topic, which has used the engine torque. The benefit of this is that no estimation of the dynamics in the powertrain is needed. The drawback of using this method is that the algorithm is dependenton the accuracy of the estimation done in the engine electrical control unit. Two different versions of the recursive least squares method (RLS) have been developed - one with a single forgetting factor and one with two forgetting factors. The estimation performance of the two versions are compared on several different real-world driving scenarios, which include driving on country roads, highways, and city roads, and different loads in the vehicle. The algorithm with a single forgetting factor estimates the mass with an average error for all tests of 4.42% and the algorithm with multiple forgetting factors estimates the mass with an average error of 4.15 %, which is in line with state-of-the-art algorithms that are presented in other studies. In a sensitivity analysis, it is shown that the algorithms are robust to changes in the drag coefficient. The single forgetting factor algorithm is robust to changes in the rolling resistance coefficient whereas the multiple forgetting factor algorithm needs the rolling resistance coefficient to be estimated with fairly good accuracy. Both versions of the algorithm need to know the wheel radius with an accuracy of 90 %. The results show that the algorithms estimate the mass accurately for all three different driving scenarios and estimate highway roads best with an average error of 2.83 % and 2.69 % for the single forgetting factor algorithm and the multiple forgetting factor algorithm, respectively. The results indicate it is possible to use either algorithm in a real-world scenario, where the choice of which algorithm depends on sought-after robustness.
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Benetti, Tiago. "Estimativa robusta da frequ?ncia card?aca a partir de sinais de fotopletismografia de pulso." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8337.

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Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2018-10-29T13:30:23Z No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5)<br>Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2018-10-30T17:21:55Z (GMT) No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5)<br>Made available in DSpace on 2018-10-30T17:27:25Z (GMT). No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) Previous issue date: 2018-08-31<br>Heart rate monitoring using Photoplethysmography (PPG) signals acquired from the individuals pulse has become popular due to emergence of numerous low cost wearable devices. However, monitoring during physical activities has obstacles because of the influence of motion artifacts in PPG signals. The objective of this work is to introduce a new algorithm capable of removing motion artifacts and estimating heart rate from pulse PPG signals. Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms are proposed for an adaptive filtering structure that uses acceleration signals as reference to remove motion artifacts. The algorithm uses the Periodogram of the filtered signals to extract their heart rates, which will be used together with a PPG Signal Quality Index to feed the input of a Kalman Filter. Specific heuristics and the Quality Index collaborate so that the Kalman filter provides a heart rate estimate with high accuracy and robustness to measurement uncertainties. The algorithm was validated from the heart rate obtained from Electrocardiography signals and the proposed method with the RLS algorithm presented the best results with an absolute mean error of 1.54 beats per minute (bpm) and standard deviation of 0.62 bpm, recorded for 12 individuals performing a running activity on a treadmill with varying speeds. The results make the performance of the algorithm comparable and even better than several recently developed methods in this field. In addition, the algorithm presented a low computational cost and suitable to the time interval in which the heart rate estimate is performed. Thus, it is expected that this algorithm will improve the obtaining of heart rate in currently available wearable devices.<br>O monitoramento da frequ?ncia card?aca utilizando sinais de Fotopletismografia ou PPG (do ingl?s, Photopletismography) adquiridos do pulso de indiv?duos tem se popularizado devido ao surgimento de in?meros dispositivos wearable de baixo custo. No entanto, o monitoramento durante atividades f?sicas tem dificuldades em raz?o da influ?ncia de artefatos de movimento nos sinais de PPG. O objetivo deste trabalho ? introduzir um novo algoritmo capaz de remover artefatos de movimento e estimar a frequ?ncia card?aca de sinais de PPG de pulso. Os algoritmos do M?nimo Quadrado M?dio Normalizado ou NLMS (do ingl?s, Normalized Least Mean Square) e de M?nimos Quadrados Recursivos ou RLS (do ingl?s, Recursive Least Squares) s?o propostos para uma estrutura de filtragem adaptativa que utiliza sinais de acelera??o como refer?ncia para remover os artefatos de movimento. O algoritmo utiliza o Periodograma dos sinais filtrados para extrair suas frequ?ncias card?acas, que ser?o utilizadas juntamente com um ?ndice de Qualidade do Sinal de PPG para alimentar a entrada de um Filtro de Kalman. Heur?sticas espec?ficas e o ?ndice de Qualidade colaboram para que filtro de Kalman forne?a uma estimativa da frequ?ncia card?aca com alta acur?cia e robustez a incertezas de medi??o. O algoritmo foi validado a partir da frequ?ncia card?aca obtida de sinais de Eletrocardiografia e o m?todo proposto com o algoritmo RLS apresentou os melhores resultados com um erro m?dio absoluto de 1,54 batimentos por minuto (bpm) e desvio padr?o de 0,62 bpm, registrados para 12 indiv?duos realizando uma atividade de corrida em uma esteira com velocidades variadas. Os resultados tornam o desempenho do algoritmo compar?vel e at? mesmo melhor que v?rios m?todos desenvolvidos recentemente neste campo. Al?m disso, o algoritmo apresentou um custo computacional baixo e adequado ao intervalo de tempo em que a estimativa da frequ?ncia card?aca ? realizada. Dessa forma, espera-se que este algoritmo melhore a obten??o da frequ?ncia card?aca em dispositivos wearable atualmente dispon?veis.
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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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Sequeira, Armando M. P. de Jesus. "Adaptive two dimensional RLS algorithms." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25653.

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Huo, Jia Q. "Numerical properties of adaptive recursive least-squares (RLS) algorithms with linear constraints." Thesis, Curtin University, 1999. http://hdl.handle.net/20.500.11937/270.

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Adaptive filters have found applications in many signal processing problems. In some situations, linear constraints are imposed on the filter weights such that the filter is forced to exhibit a certain desired response. Several algorithms for linearly constrained least-squares adaptive filtering have been developed in the literature. When implemented with finite precision arithmetic, these algorithms are inevitably subjected to rounding errors. It is essential to understand how these algorithms react to rounding errors.In this thesis, the numerical properties of three linearly constrained least-squares adaptive filtering algorithms, namely, the linearly constrained fast least algorithm, the linear systolic array for MVDR beamforming and the linearly constrained QRD-RLS algorithm, are studied. It is shown that all these algorithms can be separated into a constrained part and an unconstrained part. The numerical properties of unconstrained least-squares algorithms (i.e., the unconstrained part of the linearly constrained algorithms under study) are reviewed from the perspectives of error propagation, error accumulation and numerical persistency. It is shown that persistent excitation and sufficient numerical resolution are needed to ensure the stability of the CRLS algorithm, while the QRD-RLS algorithm is unconditionally stable. The numerical properties of the constrained algorithms are then examined. Based on the technique of how the constraints are applied, these algorithms can be grouped into two categories. The first two algorithms admit a similar structure in that the unconstrained parts preceed the constrained parts. Error propagation analysis shows that this structure gives rise to unstable error propagation in the constrained part. In contrast, the constrained part of the third algorithm preceeds the unconstrained part. It is shown that this algorithm gives an exact solution to a linearly constrained least-squares adaptive filtering problem with perturbed constraints and perturbed input data. A minor modification to the constrained part of the linearly constrained QRD-RLS algorithm is proposed to avoid a potential numerical difficulty due to the Gaussian elimination operation employed in the algorithm.
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Huo, Jia Q. "Numerical properties of adaptive recursive least-squares (RLS) algorithms with linear constraints." Curtin University of Technology, Australian Telecommunications Research Institute, 1999. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10094.

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Adaptive filters have found applications in many signal processing problems. In some situations, linear constraints are imposed on the filter weights such that the filter is forced to exhibit a certain desired response. Several algorithms for linearly constrained least-squares adaptive filtering have been developed in the literature. When implemented with finite precision arithmetic, these algorithms are inevitably subjected to rounding errors. It is essential to understand how these algorithms react to rounding errors.In this thesis, the numerical properties of three linearly constrained least-squares adaptive filtering algorithms, namely, the linearly constrained fast least algorithm, the linear systolic array for MVDR beamforming and the linearly constrained QRD-RLS algorithm, are studied. It is shown that all these algorithms can be separated into a constrained part and an unconstrained part. The numerical properties of unconstrained least-squares algorithms (i.e., the unconstrained part of the linearly constrained algorithms under study) are reviewed from the perspectives of error propagation, error accumulation and numerical persistency. It is shown that persistent excitation and sufficient numerical resolution are needed to ensure the stability of the CRLS algorithm, while the QRD-RLS algorithm is unconditionally stable. The numerical properties of the constrained algorithms are then examined. Based on the technique of how the constraints are applied, these algorithms can be grouped into two categories. The first two algorithms admit a similar structure in that the unconstrained parts preceed the constrained parts. Error propagation analysis shows that this structure gives rise to unstable error propagation in the constrained part. In contrast, the constrained part of the third algorithm preceeds the unconstrained part. It is shown that this algorithm gives an ++<br>exact solution to a linearly constrained least-squares adaptive filtering problem with perturbed constraints and perturbed input data. A minor modification to the constrained part of the linearly constrained QRD-RLS algorithm is proposed to avoid a potential numerical difficulty due to the Gaussian elimination operation employed in the algorithm.
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Silva, Cristiane Cristina Sousa da. "UM ALGORITMO TIPO RLS BASEADO EM SUPERFÍCIES NÃO QUADRÁTICAS." Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/550.

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Made available in DSpace on 2016-08-17T16:54:33Z (GMT). No. of bitstreams: 1 Tese Cristiane Cristina.pdf: 4404224 bytes, checksum: a68e5757bedc2d3d341a5937f100fe1f (MD5) Previous issue date: 2013-07-19<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>In adaptive filtering many adaptive filter are based on the mean square error method (MSE). These filters were developed to improve convergence spedd with a lower misadjustment. The least mean square (LMS) and the recursive least square (RLS) algorithms have been the hallmark of adaptive filtering. In this work we develop adaptive algorithms based on the even powers of the error inspired in the recursive lest square (RLS) algorithm. Namely recursive nom quadratic (RNQ) algorithm. The ideas is based on Widrow s least mean square fourth (LMF) algorithm. Fisrt we derive equations based on a singal even power of the error in order to obtain criterions that guarantee convergence. We also determine equations that measure the misadjustment and the time constant of the adaptive process of the RNQ algorithm. We work also, toward making the algorithm less sensitive to the size of the error in na alternative direction, by proposing a cost function which is a sum of the even powers of the error. This second approach bring the error explicitly to the RLS algorithm formulation by proposing a new cost function that preserves the measnsquare-error (MSE) solution, but allows for the exploitation of higher order moments of the error to speedup the converge of the algorithm. The main goal this work is to create form first principles (new cost functions ) a mechanism to include instantaneous error information in the RLS algorithm, make it track better, and allow for the design of the forgetting factor. As we will see the key aspecto of our approach is to include the error in the Kalman gain that effectively controls the speed of adaptation of the RLS algorithm.<br>Em filtragem adaptativa, vários filtros são baseados no método do erro quadrático médio (do inglês, MSE- mean squared error ) e muitos desses foram desenvolvidos para obter uma convergência rápida com um menos desajuste. Os algoritmos mínimos quadrático médio (do inglês, LMS- least mean square ) e mínimos quadrados recursivos (do inglês, RLS- recursive least square ) foram um marco em filtragem adaptativa. Nesse trabalho apresentamos o desenvolvimento de uma família de algoritmos adaptativos baseados nas potências pares do erro, inspirado na dedução do algoritmo RLS padrão. Chamaremos esses novos algoritmos de recursivo não-quadrático (RNQ). A ideia básica é baseada na função de custo apresentada por Widrow no algoritmo mínimo quarto médio ( do inglês, LMF least mean square fourth). Inicialmente derivamos equações baseados em uma potência par do erro para obter critérios que garantam a convergência. Determinamos também, equações que definem o desajuste e o tempo de aprendizagem do processo de adaptação do algoritmo RNQ baseado em potência para arbitrária. Trabalhamos também, no sentido de tornar o algoritmo menos sensível ao tamanho do erro numa direção alternativa, propondo uma função de custo baseado na soma das potências pares do erro. Essa segunda abordagem torna explícito o papel do erro na formulação do RLS ao propor uma nova função de custo que preserve a solução MSE, mas permite a utilização dos momentos de alta ordem do erro para aumentar a velocidade de convergência do algoritmo. O principal objetivo do nosso trabalho é criar a partir dos primeiros princípios (novas funções de custo) um mecanismo para incluir informações de erro instantâneo no algoritmo RLS e torná-lo um seguidor melhor. Assim, o aspecto-chave dessa nova abordagem é incluir o erro no ganho de Kalman que controla efetivamente a velocidade de adaptação do algoritmo de RLS.
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Karlsson, Erlendur. "Least squares arma modeling of linear time-varying systems : lattice filter structures and fast RLS algorithms." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/15936.

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Books on the topic "RLS Algorithm"

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Tobia, John. A time-varying analysis of the exponentially data weighted recursive least squares (EDW-RLS) algorithm. National Library of Canada, 1992.

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Armando M. P. de Jesus Sequeira. Adaptive two dimensional RLS algorithms. Naval Postgraduate School, 1989.

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Nair, Raveendranath U., Hema Singh, Vineetha Joy, and Vishal G. Padwal. Optimization of Multilayered Radar Absorbing Structures (RAS) Using Nature Inspired Algorithm. Taylor & Francis Group, 2021.

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Nair, Raveendranath U., Hema Singh, Vineetha Joy, and Vishal G. Padwal. Optimization of Multilayered Radar Absorbing Structures (RAS) Using Nature Inspired Algorithm. Taylor & Francis Group, 2021.

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Optimization of Multilayered Radar Absorbing Structures (RAS) Using Nature Inspired Algorithm. Taylor & Francis Group, 2021.

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Optimization of Multilayered Radar Absorbing Structures (RAS) Using Nature Inspired Algorithm. Taylor & Francis Group, 2023.

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ML, Sowjanya, and Vinayaka VM. Design and Analysis of Algorithms Lab Manual for Diploma in Karnataka: RJS Polytechnic. Independently Published, 2018.

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Book chapters on the topic "RLS Algorithm"

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Chern, Shiunn-Jang. "Linear Constrained QRD-Based Algorithm." In QRD-RLS Adaptive Filtering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-09734-3_12.

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Diniz, Paulo S. R. "Quantization Effects in the RLS Algorithm." In Adaptive Filtering. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_16.

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Lim, Junseok, Joonil Song, and Yonggook Pyeon. "A Prewhitening RLS Projection Alternated Subspace Tracking (PAST) Algorithm." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_199.

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Morgül, Ö., and A. Malaş. "Application of Gauss-Seidel Iteration to the RLS Algorithm." In Linear Algebra for Large Scale and Real-Time Applications. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8196-7_47.

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Luan, Yanqiang. "Multiresolution Traffic Prediction: Combine RLS Algorithm with Wavelet Transform." In Information Networking. Convergence in Broadband and Mobile Networking. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30582-8_34.

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Gupta, V. K., D. K. Gupta, and Mahesh Chandra. "Real-Time Noise Canceller Using Modified Sigmoid Function RLS Algorithm." In Computational Vision and Robotics. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2196-8_8.

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Sharma, Raghavendra, and V. Prem Pyara. "Signature Wavelet Identification of Sounds of Musical Instruments Using RLS Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1540-3_27.

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Ghanassi, Mohamed, and Benoit Champagne. "Subband Acoustic Echo Cancellation Using the FAP-RLS Algorithm: Fixedpoint Implementation Issues." In Acoustic Signal Processing for Telecommunication. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8644-3_3.

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Skarbek, Władysław, Adam Pietrowcew, and Radosław Sikora. "Modified Oja-RLS algorithm—Stochastic convergence analysis and application for image compression." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0095127.

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Zhu, Xiao-Long, Xian-Da Zhang, and Ying Jia. "Adaptive RLS Implementation of Non-negative PCA Algorithm for Blind Source Separation." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_125.

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Conference papers on the topic "RLS Algorithm"

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Sutcliffe de Moraes, Naomi J., Vítor H. Nascimento, Daniel C. Vidal, Carlos A. Prete, and Yuriy V. Zakharov. "A faster RLS-DCD adaptive filtering algorithm." In 2024 19th International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2024. http://dx.doi.org/10.1109/iswcs61526.2024.10639158.

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Otopeleanu, Radu, Camelia Elisei-Iliescu, Constantin Paleologu, Silviu Ciochină, and Jacob Benesty. "Regularized RLS Algorithm Based on Third-Order Tensor Decomposition." In 2024 Advanced Topics on Measurement and Simulation (ATOMS). IEEE, 2024. https://doi.org/10.1109/atoms60779.2024.10921571.

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Naderahmadian, Yashar, Seyed Ghorshi, and Issa Panahi. "Single Microphone Speech Denoising Using Wavelet Thresholding and RLS Algorithm." In 2025 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2025. https://doi.org/10.1109/iceic64972.2025.10879774.

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Bellizia, Davide, Pietro Monsurro, and Alessandro Trifiletti. "VHDL implementation of FWL RLS algorithm." In 2017 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2017. http://dx.doi.org/10.1109/ecctd.2017.8093356.

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Begusic, D., D. Linebarger, E. M. Dowling, and B. Raghothaman. "Spectral line RLS adaptive filtering algorithm." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.756208.

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Te Yan Chen and Yuriy Zakharov. "Convergence analysis of RLS-DCD algorithm." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278616.

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Linghui Wang, Wei He, Kaihong Zhou, and Zhen Huang. "Adaptive channel equalization based on RLS algorithm." In 2011 International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM). IEEE, 2011. http://dx.doi.org/10.1109/icssem.2011.6081250.

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Liu, X., P. Baylou, and M. Najim. "A new 2D fast lattice RLS algorithm." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226234.

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Zhang, Sheng, and Jiashu Zhang. "An RLS algorithm with evolving forgetting factor." In 2015 Seventh International Workshop on Signal Design and its Applications in Communications (IWSDA). IEEE, 2015. http://dx.doi.org/10.1109/iwsda.2015.7458406.

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Sharif, Bayan S., Teong C. Chuah, Oliver R. Hinton, and Shihab A. Jimaa. "Nonlinear RLS algorithm for impulsive CDMA channels." In ITCom 2003, edited by Mohammed Atiquzzaman and Mahbub Hassan. SPIE, 2003. http://dx.doi.org/10.1117/12.511930.

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Reports on the topic "RLS Algorithm"

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Turner, Peter R. An Improved RNS Division Algorithm. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada299006.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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Winters, Bradford D. Patient Monitoring Systems To Prevent Failure To Rescue. Agency for Healthcare Research and Quality (AHRQ), 2024. https://doi.org/10.23970/ahrqepc_mhs4monitoring.

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Objectives. To review the evidence published after the previous Making Healthcare Safer (MHS) report on the effectiveness of implementing patient monitoring systems that scan patient data for signs of clinical deterioration to alert a clinician of a potential adverse condition. Methods. We searched PubMed and the Cochrane library for systematic reviews and primary studies, published from January 2018 to April 2024, of patient monitoring systems reporting the activation of a rapid response system (RRS), incidence of cardiorespiratory arrest, hospital mortality, transition to higher level of care, hospital length of stay, measurement of alarm/alert fatigue, or serious adverse events. Findings. We retrieved 4,120 citations, of which 33 articles were eligible for review (3 systematic reviews and 30 primary studies). Three categories of interventions were identified: implementing a new standard of care for patient monitoring; a change in monitoring modality; or a change in parameters monitored and/or implementation of a scoring system and/or application of an artificial intelligence algorithm and/or implementation of a communication cascade. Implementation of an early warning system alone did not improve the relevant outcomes. In primary studies of adults, none of the interventions showed consistent improvement in relevant outcomes (low/ insufficient strength of evidence). However, moderate evidence suggests that changing the monitoring modality may reduce transfers to higher levels of care and length of hospital stay, and modifying monitored parameters was associated with decreased hospital mortality (moderate evidence). There was insufficient evidence for pediatric populations. Conclusions. It is unclear whether early warning systems improve outcomes by using continuous monitoring for vital sign data acquisition, automating early warning score calculation, applying artificial intelligence algorithms, changing the alert communication cascade or changing the standard of care for monitoring.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Соловйов, Володимир Миколайович, Vladimir Saptsin, and Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, 2009. http://dx.doi.org/10.31812/0564/1131.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&amp;P 500, RTS, PFTS and other.
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A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, 2020. http://dx.doi.org/10.4271/2020-01-5154.

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At present, with the development of Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), the decision-making for automated vehicle based on connected environment conditions has attracted more attentions. Reliability, efficiency and generalization performance are the basic requirements for the vehicle decision-making system. Therefore, this paper proposed a decision-making method for connected autonomous driving based on Wasserstein Generative Adversarial Nets-Deep Deterministic Policy Gradient (WGAIL-DDPG) algorithm. In which, the key components for reinforcement learning (RL) model, reward function, is designed from the aspect of vehicle serviceability, such as safety, ride comfort and handling stability. To reduce the complexity of the proposed model, an imitation learning strategy is introduced to improve the RL training process. Meanwhile, the model training strategy based on cloud computing effectively solves the problem of insufficient computing resources of the vehicle-mounted system. Test results show that the proposed method can improve the efficiency for RL training process with reliable decision making performance and reveals excellent generalization capability.
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