Relevant bibliographies by topics / Recursive least squares(RLS)

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Academic literature on the topic 'Recursive least squares(RLS)'

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Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Recursive least squares(RLS)"

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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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Zhang, Xu Dong, Ji Fu Guan, and Liang Gu. "Realization and Comparison of System Identification Based on Different Least Squares Methods." Applied Mechanics and Materials 226-228 (November 2012): 2167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2167.

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System identification, which includes parameter identification and non-parameter identification, is to estimate its mathematical model based on the input and output observation in system. This paper discusses the system identification theory and establishes a transfer function of 1/4 vehicle’s second-order vibration system model. Through the discrete transfer function, the system’s difference equation can be obtained to identify the system in two ways, RLS (recursive least squares) and RELS (extended recursive least squares). Finally, the paper makes a comparative analysis about RLS and RELS in connection with the vehicle model. The results show that RELS method is more accurate and has stronger convergence than RLS method, which provides the basis for the researching of control system’s algorithm, simulation and making control strategy.
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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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MOONEN, MARC. "SYSTOLIC ALGORITHMS FOR RECURSIVE TOTAL LEAST SQUARES PARAMETER ESTIMATION AND MIXED RLS/RTLS PROBLEMS." International Journal of High Speed Electronics and Systems 04, no. 01 (1993): 55–68. http://dx.doi.org/10.1142/s0129156493000042.

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Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of efficient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as well as linearly constrained and "mixed RLS/RTLS" problems are considered.
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Elisei-Iliescu, Camelia, Laura-Maria Dogariu, Constantin Paleologu, Jacob Benesty, Andrei-Alexandru Enescu, and Silviu Ciochină. "A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms." Algorithms 13, no. 6 (2020): 135. http://dx.doi.org/10.3390/a13060135.

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High-dimensional system identification problems can be efficiently addressed based on tensor decompositions and modelling. In this paper, we design a recursive least-squares (RLS) algorithm tailored for the identification of trilinear forms, namely RLS-TF. In our framework, the trilinear form is related to the decomposition of a third-order tensor (of rank one). The proposed RLS-TF algorithm acts on the individual components of the global impulse response, thus being efficient in terms of both performance and complexity. Simulation results indicate that the proposed solution outperforms the conventional RLS algorithm (which handles only the global impulse response), but also the previously developed trilinear counterparts based on the least-mean- squares algorithm.
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Fîciu, Ionuț-Dorinel, Cristian-Lucian Stanciu, Cristian Anghel, and Camelia Elisei-Iliescu. "Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms." Applied Sciences 11, no. 18 (2021): 8656. http://dx.doi.org/10.3390/app11188656.

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Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.
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Zhang, Jinliang, Longyun Kang, Lingyu Chen, and Zhihui Xu. "Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/567492.

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This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs.
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Sitjongsataporn, Suchada. "Variable Tap-Length Mixed-Tone RLS-based Per-Tone Equalisation with Adaptive Implementation." ECTI Transactions on Electrical Engineering, Electronics, and Communications 10, no. 2 (2011): 179–88. http://dx.doi.org/10.37936/ecti-eec.2012102.170366.

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In this paper, a methodology of mixed-tone recursive least squares algorithm development based on an orthogonal projection approach and a new variable tap-length mechanism is presented for per-tone equalisation (PTEQ) in discrete multitone systems. A mixed-tone cost function described as the sum of weight estimated errors is minimised to achieve the solutions for different per-tone equalisers simultaneously. We describe about the inverse square-root recursive least squares algorithm based upon the QRdecomposition which preserves the Hermitian symmetry of the inverse autocorrelation matrix by means of the product of square-root matrix and its Hermitian transpose. Such symmetrical property lends the benefit to the parallel implementation. In order to reduce the computational complexity, a new variable tap-length algorithm based on the sense of mean square mixted-tone errors is introduced to search a proper choice of tap-length of PTEQ. Simulation results show the improvement of achievable bit rate and signal to noise ratio performance as compared to the PTEQ exploiting conventional recursive least squares algorithm.
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Chakraborty, Parnasree, Syed Rafiammal, A. Ambika, and Idhikash Raja. "Analysis of Audio Filtering Algorithms for Noise Cancellation." December 2023 5, no. 4 (2023): 351–67. http://dx.doi.org/10.36548/jei.2023.4.001.

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Real-time signal noise reduction poses a significant challenge in communication systems. To combat intrusive background noise during portable device conversations, an active noise cancellation (ANC) is employed. ANC generates anti-noise signals using techniques such as Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms, Recursive Least Squares (RLS) and modified Recursive Least Squares (M-RLS) for effective reduction of unwanted sound and enhancing the output signal quality. The study includes various real-time noise signals to create anti-noise responses, successfully negating corresponding noise in input signals, which include sine waves, live voices, and music. Through a dedicated micro-controller device and electronic network, an "Anti-noise" wave is introduced to achieve active noise control (ANC). This research identifies ANC module specifications and effectively transmits them through the ANC library, demonstrating the efficiency of the proposed noise reduction technique.
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Mahadi, Maaz, Tarig Ballal, Muhammad Moinuddin, and Ubaid M. Al-Saggaf. "A Recursive Least-Squares with a Time-Varying Regularization Parameter." Applied Sciences 12, no. 4 (2022): 2077. http://dx.doi.org/10.3390/app12042077.

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Recursive least-squares (RLS) algorithms are widely used in many applications, such as real-time signal processing, control and communications. In some applications, regularization of the least-squares provides robustness and enhances performance. Interestingly, updating the regularization parameter as processing data continuously in time is a desirable strategy to improve performance in applications such as beamforming. While many of the presented works in the literature assume non-time-varying regularized RLS (RRLS) techniques, this paper deals with a time-varying RRLS as the parameter varies during the update. The paper proposes a novel and efficient technique that uses an approximate recursive formula, assuming a slight variation in the regularization parameter to provide a low-complexity update method. Simulation results illustrate the feasibility of the derived formula and the superiority of the time-varying RRLS strategy over the fixed one.
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Dissertations / Theses on the topic "Recursive least squares(RLS)"

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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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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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Maciel, Allan James Ferreira. "CONVERGÊNCIA DO ESTIMADOR RLS PARA ALGORITMOS DE PROGRAMAÇÃO DINÂMICA HEURÍSTICA." Universidade Federal do Maranhão, 2012. http://tedebc.ufma.br:8080/jspui/handle/tede/494.

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Made available in DSpace on 2016-08-17T14:53:22Z (GMT). No. of bitstreams: 1 Dissertacao Allan James.pdf: 3170694 bytes, checksum: 054a9e74e81a7c2099800246d0b6c530 (MD5) Previous issue date: 2012-09-28<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>The union of methodologies for optimal control and dynamics programming has stimulated the development of algorithms for realization of discrete control systems of the type linear quadratic regulator (DLQR). The methodology is based on reinforcement learning methods based on temporal differences and approximate dynamic programming. The proposed method combines the approach of the value function by method RLS (recursive least squares) and approximate policy iteration schemes heuristic dynamic programming (HDP). The approach is directed to the assessment of convergence of the solution DLQR and the heuristic weighting matrices 􀜳 and 􀜴 of the utility function associated with DLQR. The investigation of convergence properties related to consistency, persistent excitation and polarization of the RLS estimator is performed. The methodology involved in a project achievements online DLQR controllers and is evaluated in a fourth order multivariable dynamic system.<br>A união das metodologias de controle ótimo e de programação dinâmica tem impulsionado o desenvolvimento de algoritmos para realizações de sistemas de controle discreto do tipo regulador linear quadrático (DLQR). A metodologia utilizada neste trabalho é fundamentada sobre métodos de aprendizagem por reforço baseados em diferenças temporais e programação dinâmica aproximada. O método proposto combina a aproximação da função valor através do método RLS (mínimos quadrados recursivos) e iteração de política aproximada em esquemas de programação dinâmica heurística (HDP). A abordagem é orientada para a avaliação da convergência da solução DLQR e para a sintonia heurística das matrizes de ponderação 􀜳 e 􀜴da função de utilidade associada ao DLQR. É realizada a investigação das propriedades de convergência relacionadas à consistência, excitação persistente e polarização do estimador RLS. A metodologia contempla realizações de projetos de forma online de controladores DLQR e é avaliada em um sistema dinâmico multivariável de quarta ordem.
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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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Ferreira, Ernesto Franklin Marçal. "Melhorias de estabilidade numérica e custo computacional de aproximadores de funções valor de estado baseados em estimadores RLS para projeto online de sistemas de controle HDP-DLQR." Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/1687.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-23T20:34:27Z No. of bitstreams: 1 ErnestoFerreira.pdf: 1744167 bytes, checksum: c125c90e5eb2aab2618350567f88cb31 (MD5)<br>Made available in DSpace on 2017-06-23T20:34:27Z (GMT). No. of bitstreams: 1 ErnestoFerreira.pdf: 1744167 bytes, checksum: c125c90e5eb2aab2618350567f88cb31 (MD5) Previous issue date: 2016-03-08<br>The development and the numerical stability analysis of a new adaptive critic algorithm to approximate the state-value function for online discrete linear quadratic regulator (DLQR) optimal control system design based on heuristic dynamic programming (HDP) are presented in this work. The proposed algorithm makes use of unitary transformations and QR decomposition methods to improve the online learning e-ciency in the critic network through the recursive least-squares (RLS) approach. The developed learning strategy provides computational performance improvements in terms of numerical stability and computational cost which aim at making possible the implementations in real time of optimal control design methodology based upon actor-critic reinforcement learning paradigms. The convergence behavior and numerical stability of the proposed online algorithm, called RLSµ-QR-HDP-DLQR, are evaluated by computational simulations in three Multiple-Input and Multiple-Output (MIMO) models, that represent the automatic pilot of an F-16 aircraft of third order, a fourth order RLC circuit with two input voltages and two controllable voltage levels, and a doubly-fed induction generator with six inputs and six outputs for wind energy conversion systems.<br>Neste trabalho, apresenta-se o desenvolvimento e a análise da estabilidade numérica de um novo algoritmo crítico adaptativo para aproximar a função valor de estado para o projeto do sistema de controle ótimo online, utilizando o regulador linear quadrático discreto (DLQR), com base em programação dinâmica heurística (HDP). O algoritmo proposto faz uso de transformações unitárias e métodos de decomposição QR para melhorar a e-ciência da aprendizagem online na rede crítica por meio da abordagem dos mínimos quadrados recursivos (RLS). A estratégia de aprendizagem desenvolvida fornece melhorias no desempenho computacional em termos de estabilidade numérica e custo computacional, que visam tornar possíveis as implementações em tempo real da metodologia do projeto de controle ótimo com base em paradigmas de aprendizado por reforço ator-crítico. O comportamento de convergência e estabilidade numérica do algoritmo online proposto, denominado RLSµ-QR-HDP-DLQR, são avaliados por meio de simulações computacionais em três modelos Múltiplas-Entradas e Múltiplas-Saídas (MIMO), que representam o piloto automático de uma aeronave F-16 de terceira ordem, um circuito de quarta ordem RLC com duas tensões de entrada e dois níveis de tensão controláveis, e um gerador de indução duplamente alimentados com seis entradas e seis saídas para sistemas de conversão de energia eólica.
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Halldin, Axel. "Control of a Multivariable Lighting System." Thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-134913.

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This master’s thesis examines how a small MIMO lighting system can be identified and controlled. Two approaches are examined and compared; the first approach is a dynamic model using state space representation, where the system identification technique is Recursive Least Square, RLS, and the controller is an LQG controller; the second approach is a static model derived from the physical properties of light and a feedback feed-forward controller consisting of a PI controller coupled with a Control Allocation, CA, technique. For the studied system, the CA-PI approach significantly outperforms the LQG-RLS approach, which leads to the conclusion that the system’s static properties are predominant compared to the dynamic properties.
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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.

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Bian, Xiaomeng. "Completely Recursive Least Squares and Its Applications." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1518.

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The matrix-inversion-lemma based recursive least squares (RLS) approach is of a recursive form and free of matrix inversion, and has excellent performance regarding computation and memory in solving the classic least-squares (LS) problem. It is important to generalize RLS for generalized LS (GLS) problem. It is also of value to develop an efficient initialization for any RLS algorithm. In Chapter 2, we develop a unified RLS procedure to solve the unconstrained/linear-equality (LE) constrained GLS. We also show that the LE constraint is in essence a set of special error-free observations and further consider the GLS with implicit LE constraint in observations (ILE-constrained GLS). Chapter 3 treats the RLS initialization-related issues, including rank check, a convenient method to compute the involved matrix inverse/pseudoinverse, and resolution of underdetermined systems. Based on auxiliary-observations, the RLS recursion can start from the first real observation and possible LE constraints are also imposed recursively. The rank of the system is checked implicitly. If the rank is deficient, a set of refined non-redundant observations is determined alternatively. In Chapter 4, base on [Li07], we show that the linear minimum mean square error (LMMSE) estimator, as well as the optimal Kalman filter (KF) considering various correlations, can be calculated from solving an equivalent GLS using the unified RLS. In Chapters 5 & 6, an approach of joint state-and-parameter estimation (JSPE) in power system monitored by synchrophasors is adopted, where the original nonlinear parameter problem is reformulated as two loosely-coupled linear subproblems: state tracking and parameter tracking. Chapter 5 deals with the state tracking which determines the voltages in JSPE, where dynamic behavior of voltages under possible abrupt changes is studied. Chapter 6 focuses on the subproblem of parameter tracking in JSPE, where a new prediction model for parameters with moving means is introduced. Adaptive filters are developed for the above two subproblems, respectively, and both filters are based on the optimal KF accounting for various correlations. Simulations indicate that the proposed approach yields accurate parameter estimates and improves the accuracy of the state estimation, compared with existing methods.
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Hutchinson, Derek Charles Glenn. "Manipulator inverse kinematics based on recursive least squares estimation." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/27890.

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The inverse kinematics problem for six degree of freedom robots having a separable structure with the wrist equivalent to a spherical joint is considered and an iterative solution based on estimating the inverse Jacobian by recursive least squares estimation is proposed. This solution is found to have properties similar to Wampler's Damped Least Squares method and provides a stable result when the manipulator is in singular regions. Furthermore, the solution is more computationally efficient than Wampler's method; however, its best performance is obtained when the distances between the current end effector pose and the target pose are small. No knowledge of the manipulator's geometry is required provided that the end effector and joint position data are obtained from sensor information. This permits the algorithm to be readily transferable among manipulators and circumvents detailed analysis of the manipulator's structure.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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Books on the topic "Recursive least squares(RLS)"

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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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United States. National Aeronautics and Space Administration., ed. On recursive least-squares filtering algorithms and implementations. University of California, 1990.

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United States. National Aeronautics and Space Administration., ed. On recursive least-squares filtering algorithms and implementations. University of California, 1990.

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Olszanskyj, Serge. Rank-k modification for recursive least squares problems. Cornell Theory Center, Cornell University, 1993.

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Walke, Richard Lewis. High sample-rate Givens rotations for recursive least squares. typescript, 1997.

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Price, Lydia J. Recursive least-squares approach to data transferability: Exposition and numerical results. INSEAD, 1992.

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Center, Ames Research, ed. Round-off error propogation in four generally applicable, recursive, least-squares-estimation schemes. National Aeronautics and Space Administration, Ames Research Center, 1988.

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Vaez-Zadeh, Sadegh. Parameter Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0007.

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In this chapter, the estimation of permanent magnetic synchronous (PMS) motor parameters, including stator winding resistance, motor inductances, and magnitude of permanent magnet flux linage, is presented in two main categories, i.e., offline and online. Several offline schemes, including DC and AC standstill tests, no-load test, load test, and vector control schemes, are presented for estimation of all the motor parameters. Major online schemes used in the estimation of PMS motor parameters are also presented in this chapter. They include closed-loop observer-based estimation, model reference adaptive system (MRAS)-based estimation, recursive least-squares (RLS) estimation, and extended Kalman filter scheme. The online schemes take into account the motor parameter variations during motor operation. The motor model, estimation procedure, and the connection of estimation systems to the motor control system are discussed for each parameter estimation scheme.
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Dynamic data processing: Recursive least-squares. Delft University Press, 2001.

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10

On recursive least-squares filtering algorithms and implementations. University of California, 1990.

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Book chapters on the topic "Recursive least squares(RLS)"

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Mohammadazadeh, Ardahir, Mohammad Hosein Sabzalian, Oscar Castillo, Rathinasamy Sakthivel, Fayez F. M. El-Sousy, and Saleh Mobayen. "Neural Networks Training Based on Recursive Least Squares (RLS)." In Synthesis Lectures on Intelligent Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14571-1_3.

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Lima, Alanio F., Laurinda L. N. dos Reis, Darielson A. Souza, et al. "Recursive Least Squares Identification with Extreme Learning Machine (RLS-ELM)." In Information Systems and Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45642-8_45.

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Prasad, Kantipudi M. V. V., and H. N. Suresh. "Spectral Estimation Using Improved Recursive Least Square (RLS) Algorithm: An Investigational Study." In Emerging Research in Computing, Information, Communication and Applications. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2550-8_36.

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Yuan, Jenq-Tay. "QRD Least-Squares Lattice Algorithms." In QRD-RLS Adaptive Filtering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-09734-3_5.

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Benesty, Jacob, Constantin Paleologu, Tomas Gänsler, and Silviu Ciochină. "Recursive Least-Squares Algorithms." In A Perspective on Stereophonic Acoustic Echo Cancellation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22574-1_6.

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Young, Peter C. "Recursive Least Squares Estimation." In Recursive Estimation and Time-Series Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21981-8_3.

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Strobach, Peter. "Recursive Least-Squares Transversal Algorithms." In Springer Series in Information Sciences. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75206-3_5.

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Strobach, Peter. "Fast Recursive Least-Squares Ladder Algorithms." In Springer Series in Information Sciences. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75206-3_9.

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Zhao, Ji, and Hongbin Zhang. "Projected Kernel Recursive Least Squares Algorithm." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_38.

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Luk, Franklin T. "Fault Tolerant Recursive Least Squares Minimization." In Numerical Linear Algebra, Digital Signal Processing and Parallel Algorithms. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75536-1_12.

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Conference papers on the topic "Recursive least squares(RLS)"

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Cai, Yongjie, Dongliang Fu, Jiongmin Yu, and Wei Gao. "Stochastic Analysis of Regularized Recursive Least-Squares Algorithm." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846628.

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Mahdi, Ali Salah, Lwaa Faisal Abdulameer та Ameer Hussein Morad. "Comparison between Epsilon Normalized Least Means Square (ϵ-NLMS) and Recursive Least Squares (RLS) Adaptive Algorithms". У 2018 International Conference on Computing Sciences and Engineering (ICCSE). IEEE, 2018. http://dx.doi.org/10.1109/iccse1.2018.8374208.

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Goetze, Juergen. "Iterative version of the QRD for adaptive recursive least squares (RLS) filtering." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Franklin T. Luk. SPIE, 1994. http://dx.doi.org/10.1117/12.190856.

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Ilyas, Mohd Zaizu, Salina Abdul Samad, Aini Hussain, and Khairul Anuar Ishak. "Enhancing speaker verification in noisy environments using Recursive Least-Squares (RLS) adaptive filter." In 2008 International Symposium on Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/itsim.2008.4631877.

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Monsurro, Pietro, and Alessandro Trifiletti. "The recursive batch least squares filter: An efficient RLS filter for floating-point hardware." In 2017 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2017. http://dx.doi.org/10.1109/ecctd.2017.8093223.

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Mugdha, Arya Chowdhury, Ferdousi Sabera Rawnaque, and Mosabber Uddin Ahmed. "A study of recursive least squares (RLS) adaptive filter algorithm in noise removal from ECG signals." In 2015 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2015. http://dx.doi.org/10.1109/iciev.2015.7333998.

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Wang, Yina, Renpeng Tan, Yinlai Jiang, Shuoyu Wang, and Kazuhiro Hamaguchi. "Digital acceleration controller based on recursive least squares (RLS) identification for an excretion care support robot." In 2012 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2012. http://dx.doi.org/10.1109/icma.2012.6284382.

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Orzechowski, Pawel Konrad, Tsu-Chin Tsao, and James Steve Gibson. "The Effect of Computational Delay on Performance of Adaptive Control Systems." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15255.

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In many adaptive control applications, especially where the recursive-least-squares (RLS) algorithms are used, the real-time implementation of high order adaptive filters for estimating the disturbance dynamics is computationally intensive. The delay associated with the computational burden is usually either underestimated as no delay or overestimated as one sample delay in the control system design and analysis. For a stochastic disturbance dynamics, the H2 optimal control performance for the case of one-step delay is worse than that of no delay due to the nonminimum phase plant zero introduced by the delay. The optimal performance for a fractional delay is bounded between these two extremes. The paper investigates the effect of the fractional computational delay on a variable order adaptive controller based on a recursive least-squares adaptive lattice filter. The trade-off between the adaptive filter order and the computational delay is analyzed and evaluated by an example.
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Pe´rez Arancibia, Ne´stor O., Chi-Ying Lin, Tsu-Chin Tsao, and James S. Gibson. "Adaptive and Repetitive Control for Rejecting Repeatable and Non-Repeatable Runout in Rotating Devices." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43534.

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This paper presents a control scheme for rejecting both repeatable and non-repeatable runout components of disturbances occurring in rotational devices. To exemplify this method, implementation and experimental results for track following control of a computer hard disk drive (HDD) read/write heads are presented. Aiming for high performance, the control design involves two steps. The first is the design and tuning of a recursive least-squares (RLS) based scheme intended to achieve minimum variance performance. The second step integrates repetitive and adaptive control schemes in a real-time implementation to compensate for variations and changes in the disturbance dynamics. The repetitive part of this controller targets specific periodic disturbances. The adaptive part compensates for broad bandwidth stochastic disturbances. The key element in this design is the formulation of an appropriate optimization problem, solvable recursively by applying recursive adaptive algorithms. Experimental results obtained from the implementation of this method in a commercial HDD demonstrates the effectiveness of this approach.
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HAMMOND, DARYL, and JOHN D'AZZO. "Parameter adaptive multivariable flight controller using a full autoregressive moving average (ARMA) model and recursive least squares (RLS) estimation." In 29th Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-420.

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Reports on the topic "Recursive least squares(RLS)"

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Cioffi, J. M., and T. Kailath. An Efficient, RLS (Recursive-Least-Squares) Data-Driven Echo Canceller for Fast Initialization of Full-Duplex Data Transmission,. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160177.

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