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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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Denno, Satoshi, and Yoichi Saito. "Adaptive phase control using recursive least squares (RLS) phase estimation." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 77, no. 8 (1994): 93–104. http://dx.doi.org/10.1002/ecjc.4430770809.
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Zhang, Chunyuan, Qi Song, and Zeng Meng. "Minibatch Recursive Least Squares Q-Learning." Computational Intelligence and Neuroscience 2021 (October 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/5370281.
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The deep Q-network (DQN) is one of the most successful reinforcement learning algorithms, but it has some drawbacks such as slow convergence and instability. In contrast, the traditional reinforcement learning algorithms with linear function approximation usually have faster convergence and better stability, although they easily suffer from the curse of dimensionality. In recent years, many improvements to DQN have been made, but they seldom make use of the advantage of traditional algorithms to improve DQN. In this paper, we propose a novel Q-learning algorithm with linear function approximation, called the minibatch recursive least squares Q-learning (MRLS-Q). Different from the traditional Q-learning algorithm with linear function approximation, the learning mechanism and model structure of MRLS-Q are more similar to those of DQNs with only one input layer and one linear output layer. It uses the experience replay and the minibatch training mode and uses the agent’s states rather than the agent’s state-action pairs as the inputs. As a result, it can be used alone for low-dimensional problems and can be seamlessly integrated into DQN as the last layer for high-dimensional problems as well. In addition, MRLS-Q uses our proposed average RLS optimization technique, so that it can achieve better convergence performance whether it is used alone or integrated with DQN. At the end of this paper, we demonstrate the effectiveness of MRLS-Q on the CartPole problem and four Atari games and investigate the influences of its hyperparameters experimentally.
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Pratiwi, Nor Kumalasari Caecar, Rita Magdalena, Yunendah Nur Fuadah, Sofia Saidah, Syamsul Rizal, and Muhamad Rokhmat Isnaini. "Denoising Sinyal EEG dengan Algoritma Recursive Least Square dan Least Mean Square." TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol 5, no. 2 (2019): 122–29. http://dx.doi.org/10.15575/telka.v5n2.122-129.
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EEG mengukur fluktuasi tegangan yang dihasilkan dari arus ionik yang beredar sepanjang neuron otak. Dalam pengaturan eksperimental, sinyal EEG sering terkontaminasi dengan berbagai noise akibat gerakan otot dan jantung. Noise dengan magnitudo yang lebih tinggi dari sinyal aslinya akan merusak sinyal EEG dan bisa berakibat fatal dalam analisis diagnosa. Sehingga diperlukan sebuah sistem denoising yang mampu secara maksimal mengurangi noise, tanpa menghilangkan komponen informasi penting dari sinyal EEG. Salah satu algoritma yang dapat digunakan dalam mereduksi noise pada sinyal biomedis adalah RLS dan LMS. Keuntungan utama dari penggunaan adaptif filtering termasuk RLS dan LMS adalah dapat digunakan pada lingkungan non-stasioner. Tujuan penelitian adalah melakukan uji perbandingan performansi filtering RLS dan LMS dalam mereduksi noise pada sinyal EEG. Parameter performansi yang diukur adalah waktu komputasi, MSE, SNR, dan PSNR. Dari hasil pengujian, diperoleh bahwa adaptif filtering dengan RLS dan LMS mampu mereduksi noise pada sinyal EEG dengan baik. Filter LMS memiliki kelebihan pada waktu komputasinya yang singkat, rata-rata waktu komputasi filter LMS selama 0.7 detik, jauh berbeda dengan filter RLS yang membutuhkan waktu sampai dengan 113 detik. Tetapi kehandalan sistem dari sisi MSE, SNR dan PSNR untuk filter LMS masih berada dibawah RLS untuk intensitas noise yang rendah. Besarnya parameter SNR dan PSNR pada filter RLS cenderung lebih stabil pada intesitas noise 10 dB, 20 dB, dan 30 db. Hal berbeda terjadi pada denoising dengan menggunakan filter LMS, terjadi perubahan SNR yang signifikan dari 16.14 dB pada noise 10 dB, 21.09 dB untuk noise sebesar 20 dB, dan 25.81 dB untuk intensitas noise sebesar 30 dB.
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Yousefi, Iman, and Mahmood Ghanbari. "Parameter Estimation of Permanent Magnet Synchronous Motor Using Orthogonal Projection and Recursive Least Squares Combinatorial Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/418207.
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This paper presents parameter estimation of Permanent Magnet Synchronous Motor (PMSM) using a combinatorial algorithm. Nonlinear fourth-order space state model of PMSM is selected. This model is rewritten to the linear regression form without linearization. Noise is imposed to the system in order to provide a real condition, and then combinatorial Orthogonal Projection Algorithm and Recursive Least Squares (OPA&RLS) method is applied in the linear regression form to the system. Results of this method are compared to the Orthogonal Projection Algorithm (OPA) and Recursive Least Squares (RLS) methods to validate the feasibility of the proposed method. Simulation results validate the efficacy of the proposed algorithm.
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Ye, Jing, and Zhiqiang Wu. "Real-time calibration of magnetometers based on the recursive least squares method." Journal of Physics: Conference Series 2897, no. 1 (2024): 012028. https://doi.org/10.1088/1742-6596/2897/1/012028.
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Abstract A real-time calibration method for magnetometers based on the recursive least squares (RLS) algorithm is proposed to address the issues of large data processing and lack of real-time performance in the calibration of magnetometers in complex magnetic environments using the traditional least squares (LS) algorithm. Firstly, an error model for the magnetometer is established. Then, using this error model as the measurement equation, the current measurement value is iterated from the previous measurement value through least squares iteration to update the state parameters. Finally, the effectiveness of the algorithm was verified by shaking the magnetometer in a “figure-eight” pattern. Experiments have shown that compared with the traditional least squares algorithm, the recursive least squares algorithm requires less data processing and offers faster calculation speed. Compared to LS calibration, the standard deviation of the magnetic vector modulus obtained from RLS calibration decreased by 24.4%, 17.4%, and 23.2% in three different environments, respectively. Additionally, by performing real-time detection and analysis of the state covariance matrix, it was determined that the calibration process is essentially complete after 3500 iterations.
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Raita-Hakola, A. M., and I. Pölönen. "UPDATING STRATEGIES FOR DISTANCE BASED CLASSIFICATION MODEL WITH RECURSIVE LEAST SQUARES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 163–70. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-163-2022.
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Abstract. The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach maximum classification accuracy quickly.The results show that the new self-learning MLM method can classify new classes with RLS update but with a cost of decreasing accuracy. With a larger amount of reference points, one class can be introduced with reasonable accuracy. The results of experiment B indicate that self-learning MLM can be trained with a few reference points, and the self-learning model quickly reaches accuracy results comparable with nearest-neighbour NN-MLM. It seems that the self-learning MLM could be a comparable machine learning method for the application of hyperspectral imaging and remote sensing.
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Zangeneh-Nejad, F., A. R. Amiri-Simkooei, M. A. Sharifi, and J. Asgari. "RECURSIVE LEAST SQUARES WITH REAL TIME STOCHASTIC MODELING: APPLICATION TO GPS RELATIVE POSITIONING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 531–36. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-531-2017.
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Geodetic data processing is usually performed by the least squares (LS) adjustment method. There are two different forms for the LS adjustment, namely the batch form and recursive form. The former is not an appropriate method for real time applications in which new observations are added to the system over time. For such cases, the recursive solution is more suitable than the batch form. The LS method is also implemented in GPS data processing via two different forms. The mathematical model including both functional and stochastic models should be properly defined for both forms of the LS method. Proper choice of the stochastic model plays an important role to achieve high-precision GPS positioning. The noise characteristics of the GPS observables have been already investigated using the least squares variance component estimation (LS-VCE) in a batch form by the authors. In this contribution, we introduce a recursive procedure that provides a proper stochastic modeling for the GPS observables using the LS-VCE. It is referred to as the recursive LS-VCE (RLS-VCE) method, which is applied to the geometry-based observation model (GBOM). In this method, the (co)variances parameters can be estimated recursively when the new group of observations is added. Therefore, it can easily be implemented in real time GPS data processing. The efficacy of the method is evaluated using a real GPS data set collected by the Trimble R7 receiver over a zero baseline. The results show that the proposed method has an appropriate performance so that the estimated (co)variance parameters of the GPS observables are consistent with the batch estimates. However, using the RLS-VCE method, one can estimate the (co)variance parameters of the GPS observables when a new observation group is added. This method can thus be introduced as a reliable method for application to the real time GPS data processing.
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Topan, Paris Ali, and Dinda Fardila. "ESTIMASI TEGANGAN OPEN CIRCUIT DAN RESISTANSI INTERNAL BATERAI MENGGUNAKAN ALGORITMA RECURSIVE LEAST SQUARES." Jurnal Informatika Teknologi dan Sains (Jinteks) 6, no. 4 (2024): 1201–5. https://doi.org/10.51401/jinteks.v6i4.4863.
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This study employs the Recursive Least Squares (RLS) algorithm to estimate the Open Circuit Voltage (OCV) and internal resistance (Ro) of a battery in real-time, based on measured current and voltage data. The results demonstrate that RLS can produce accurate estimates of OCV and Ro, with a Mean Squared Error (MSE) of 0.031, indicating a very small difference between the predicted and measured terminal voltage. The estimated OCV is stable, while Ro remains consistently within the range of 0.075 to 0.175 ohms, despite fluctuations in voltage due to changes in current. These findings show that the RLS algorithm can be effectively applied in battery management systems to dynamically and in real-time estimate parameters, which is crucial for applications such as electric vehicle battery monitoring and energy storage systems.
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Nakamori, Seiichi. "Robust Recursive Least-Squares Wiener Filter for Linear Continuous-Time Uncertain Stochastic Systems." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 19 (October 4, 2023): 108–17. http://dx.doi.org/10.37394/232014.2023.19.12.
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For linear continuous-time systems with uncertainties in the system and observation matrices, an original robust RLS Wiener filter is designed in this study. The robust RLS Wiener filter does not assume norm-bounded uncertainty for the system and observation matrices, in contrast to the robust Kalman filter. In the design of the robust RLS Wiener filter, the degraded signal, affected by the uncertainties in the system and observation matrices, is modeled by an autoregressive (AR) model. The system and observation matrices for the degraded signal are formulated from the relationship between the AR model of the degraded signal and the state-space model. Estimation formulas for the system and observation matrices are proposed in Section 2. The robust filtering problem is introduced based on the minimization of the mean-square value of the filtering errors for the system states. The robust filtering estimate is given as an integral transformation of the degraded observations using the impulse response function. The integral equation that an optimal impulse response function satisfies is given in Section 3. Theorem 1 presents a robust RLS Wiener filtering algorithm starting from this integral equation. The proposed robust RLS Wiener filter outperforms the existing robust Kalman filter regarding estimate accuracy, as shown by a numerical simulation example.
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Di Giamberardino, Paolo, Maria Aceto, Oliviero Giannini, and Matteo Verotti. "Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples." Actuators 7, no. 4 (2018): 74. http://dx.doi.org/10.3390/act7040074.
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The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification.
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Hatun, Metin. "ECG Noise Cancellation with Recursive Gauss-Seidel Algorithm." Karadeniz Fen Bilimleri Dergisi 14, no. 4 (2024): 2115–27. https://doi.org/10.31466/kfbd.1524020.
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Electrocardiogram (ECG) signals provide information about heart functions and some cardiac diseases. However, various interferences distort the ECG waveforms during its measurement and transmission can cause inaccurate analysis and diagnosis. So, this unwanted disturbance signals must be eliminated and an acceptable ECG signal must be extracted the noisy ECG recordings. Researchers developed several methods to overcome the undesired noises and interferences contaminated to the ECG recordings. The adaptive filtering techniques have attracted the attention of scientists due to their adaptation mechanism to time-varying nature of undesired signals. Most of the presented adaptive filtering algorithms are gradient-based and have the advantage of simple implementation, but are affected negatively by disturbance signals; for example, they can have slow convergence rates and poor steady-state properties. Least squares-based algorithms are advantageous due to their faster convergence rates and better steady-state properties. In this paper, Recursive Gauss-Seidel (RGS) algorithm, which is an alternative least squares-based method to Recursive Least Squares (RLS) algorithm with less computational complexity, is presented to obtain an acceptable waveform from noisy ECG recordings. The denoising performance of the RGS algorithm is studied and compared to the widely used gradient-based algorithms and the popular RLS algorithm.
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Martinek, Radek, Jaroslav Rzidky, Rene Jaros, Petr Bilik, and Martina Ladrova. "Least Mean Squares and Recursive Least Squares Algorithms for Total Harmonic Distortion Reduction Using Shunt Active Power Filter Control." Energies 12, no. 8 (2019): 1545. http://dx.doi.org/10.3390/en12081545.
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This paper deals with the use of least mean squares (LMS, NLMS) and recursive least squares (RLS) algorithms for total harmonic distortion (THD) reduction using shunt active power filter (SAPF) control. The article presents a pilot study necessary for the construction of our own controlled adaptive modular inverter. The objective of the study is to find an optimal algorithm for the implementation. The introduction contains a survey of the literature and summarizes contemporary methods. According to this research, only adaptive filtration fulfills our requirements (adaptability, real-time processing, etc.). The primary benefit of the paper is the study of the efficiency of two basic approaches to adaptation ((N)LMS and RLS) in the application area of SAPF control. The study examines the impact of parameter settings (filter length, convergence constant, forgetting factor) on THD, signal-to-noise ratio (SNR), root mean square error (RMSE), percentage root mean square difference (PRD), speed, and stability. The experiments are realized with real current and voltage recordings (consumer electronics such as PC source without power factor correction (PFC), HI-FI amplifier, etc.), which contain fast dynamic transient phenomena. The realized model takes into account a delay caused by digital signal processing (DSP) (the implementation of algorithms on field programmable gate array (FPGA), approximately 1–5 μs) and a delay caused by the reaction time of the proper inverter (approximately 100 μs). The pilot study clearly showed that the RLS algorithm is the most suitable for the implementation of an adaptive modular inverter because it achieved the best results for all analyzed parameters.
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Alkurawy, Lafta E. Jumaa. "Recursive least square and control for PUMA robotics." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1238–46. https://doi.org/10.11591/ijeecs.v21.i2. pp1238-1246.
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The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time. The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.
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Bai, Mingsian R., Jihjau Jeng, and Chingyu Chen. "Adaptive Order Tracking Technique Using Recursive Least-Square Algorithm." Journal of Vibration and Acoustics 124, no. 4 (2002): 502–11. http://dx.doi.org/10.1115/1.1501301.
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Order tracking technique is one of the important tools for diagnosis of rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. Resampling is generally required in the fast Fourier transform (FFT)-based methods to compromise between time and frequency resolution for varying shaft speeds. Conventional methods suffer from a number of shortcomings. In particular, smearing problems arise when closely spaced orders or crossing orders are present. Conventional methods also are ineffective for the applications involving multiple independent shaft speeds. This paper presents an adaptive order tracking technique based on the Recursive Least-Squares (RLS) algorithm to overcome the problems encountered in conventional methods. In the proposed method, the problem is treated as the tracking of frequency-varying bandpass signals. Order amplitudes can be calculated with high resolution by using the proposed method in real-time fashion. The RLS order tracking technique is applicable whether it is a single-axle or multi-axle system.
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Jumaa Alkurawy, Lafta E. "Recursive least square and control for PUMA robotics." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1238. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1238-1246.
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<p>The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time.<strong> </strong>The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.</p>
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Li, Shengyi, Qifan Xue, Dongfeng Shi, Xuanpeng Li, and Weigong Zhang. "Recursive Least Squares Based Refinement Network for Vehicle Trajectory Prediction." Electronics 11, no. 12 (2022): 1859. http://dx.doi.org/10.3390/electronics11121859.
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Trajectory prediction of surrounding objects plays a pivotal role in the field of autonomous driving vehicles. In the current rollout process, it suffers from an accumulation of errors, which has a negative impact on prediction accuracy. This paper proposes a parametric-learning recursive least-squares (RLS) method integrated with an encoder–decoder framework for trajectory prediction, named the recursive least-squares-based refinement network (RRN). Through the generation of several anchors in the future trajectory, RRN can capture both local and global motion patterns. We conducted experiments on the prevalent NGSIM and INTERACTION datasets, which contain various scenarios such as highways, intersections and roundabouts. The promising results indicate that RRN could improve the performance of the rollout trajectory prediction effectively.
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Wu, Fei-Yun, Hui-Zhong Yang, and Shengxing Liu. "A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications." Journal of the Acoustical Society of America 156, no. 4 (2024): 2250–64. http://dx.doi.org/10.1121/10.0030406.
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Recursive least squares (RLS)–based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.
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Rahman, Aviv Yuniar, Mamba’us Sa’adah, and Istiadi. "Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (2020): 286–95. http://dx.doi.org/10.29207/resti.v4i2.1667.
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Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.
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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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Nakamori, Seiichi. "Recursive Least-Squares Wiener Consensus Filter and Fixed-Point Smoother in Distributed Sensor Networks." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 19 (February 22, 2023): 1–12. http://dx.doi.org/10.37394/232014.2023.19.1.
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Distributed Kalman filter (DKF) is classified into the information fusion Kalman filter (IFKF), i. e. the centralized Kalman filter (CKF), and the Kalman consensus filter (KCF) in distributed sensor networks. The KCF has the advantage to improve the estimate of the state at the sensor node uniformly by incorporating the information of the observations and the filtering estimates at the neighbor nodes. In the first devised KCF, a user adjusts the consensus gain. This paper designs the recursive least-squares (RLS) Wiener consensus filter and fixed-point smoother that do not need to be adjusted in linear discrete-time stochastic systems. In addition to the observation equation at the sensor node, an observation equation is introduced excessively. Here, the new observation is the sum of the filtering estimates of the signals at the neighbor nodes of the sensor node. Thus, it is interpreted that the RLS Wiener consensus estimators incorporate the information of the observations at the neighbor nodes indirectly because the observations are used in the calculations of the filtering estimates. A numerical simulation example shows that the proposed RLS Wiener consensus filter and fixed-point smoother are superior in estimation accuracy to the RLS Wiener estimators.
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Nakamori, Seiichi. "Robust Recursive Least-Squares Finite Impulse Response Filter in Linear Continuous-Time Stochastic Systems with Uncertainties." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 20 (December 27, 2024): 92–108. https://doi.org/10.37394/232014.2024.20.11.
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The current research designs an original robust recursive least-squares (RLS) finite impulse response (FIR) filter for linear continuous-time systems with uncertainties in both the system and observation matrices. These uncertainties in the state-space model generate the degraded signal and observed value. The robust RLS FIR filter does not account for the norm-bounded uncertainties in the system and observation matrices. This study uses an observable companion form to represent the degraded signal state-space model. The system and observation matrices are estimated based on the author's previous computational methods. The robust RLS FIR filtering problem aims to minimize the mean-square errors in FIR filtering for the system state. The robust FIR filtering estimate is formulated as an integral transformation of the degraded observations using an impulse response function. Section 3 obtains the integral equation satisfied by the optimal impulse response function. Theorem 1 presents the robust RLS FIR filtering algorithms for the signal and the system state. This integral equation derives the robust RLS-FIR filtering algorithms. Numerical simulation examples show the validity of the proposed robust RLS FIR filter.
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Zhu, Jie Ping, Yong Xiang Zhang, Shuai Zhang, and Xiao Lin Wang. "Simulation Research on Rolling Element Bearing Feature Extraction Based on Recursive Least-Squares Lattice-Ladder Algorithms." Applied Mechanics and Materials 548-549 (April 2014): 481–86. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.481.
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In order to extract the weak fault information from complicated vibration signal of rolling element bearing, the Recursive Least-Squares (RLS) Lattice-Ladder Algorithms is introduced into the field of rolling bearing feature extraction. An adaptive feature extraction method is proposed. The RLS Lattice-Ladder algorithms and its adaptive filter property in the process of feature extraction were discussed. The rolling bearing vibration signal was refined by the RLS Lattice-Ladder filter method, and the refined vibration signal was demodulated by square envelope, then the rolling bearing’s characteristic fault frequency was identified by enveloped normalized amplitude-frequency spectrum. Simulation results show that compared with the LMS filter method, this method can identify fault frequency more quickly and more effectively.
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Nakamori, Seiichi. "Robust Recursive Least-Squares Fixed-Point Smoother and Filter using Covariance Information in Linear Continuous-Time Stochastic Systems with Uncertainties." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 20 (May 13, 2024): 56–66. http://dx.doi.org/10.37394/232014.2024.20.2.
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This study develops robust recursive least-squares (RLS) fixed-point smoothing and filtering algorithms for signals in linear continuous-time stochastic systems with uncertainties. The algorithms use covariance information, such as the cross-covariance function of the signal with the observed value and the autocovariance function of the degraded signal. A finite Fourier cosine series expansion approximates these functions. Additive white Gaussian noise is present in the observation of the degraded signal. A numerical simulation compares the estimation accuracy of the proposed robust RLS filter with the robust RLS Wiener filter, showing similar mean square values (MSVs) of the filtering errors. The MSVs of the proposed robust RLS fixed-point smoother are also compared to those of the proposed robust RLS filter.
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Zhenhua, Shao, Chen Tianxiang, Chen Li-an, et al. "Power Quality Disturbance Location Method based on Cross-Feedback - Recursive Least Squares." Open Electrical & Electronic Engineering Journal 9, no. 1 (2015): 208–15. http://dx.doi.org/10.2174/1874129001409010208.
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Due to the randomness and complexity of power quality disturbances, there is lack of mature and reliable detection and analysing methods on power quality disturbance, especially in the construction site with the changeable operation condition .In order to deal with the problems of non-stationary power quality signals and spectrum leakage, a new CF Recursive Least Squares (CF-RLS) based on blind sources separation method is proposed in this paper. Furthermore the way of converging on the proposed method is based on the maximum negative entropy gradient value. In this way, the verges can be detected and the CF-RLS method can meet the requirement of signal reconstruction condition. With the help of Matlab 7.0, the simulation cases with the power system harmonics with single disturbance and mixed disturbance are discussed. Moreover the simulation results show that the harmonics parameters, including amplitudes, phase angles and disturbance time, can be detected precisely. At last, the proposed method can completely meet the requirements of the power quality disturbance location.
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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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Stanciu, Cristian-Lucian, Cristian Anghel, Ionuț-Dorinel Fîciu, Camelia Elisei-Iliescu, Mihnea-Radu Udrea, and Lucian Stanciu. "On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods." Electronics 13, no. 8 (2024): 1479. http://dx.doi.org/10.3390/electronics13081479.
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Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.
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Ramírez-Chavarría, Roberto Giovanni, and Daan Y. Maldonado-Uriostigue. "A Virtual Laboratory on Recursive Least-Squares Estimation for Undergraduate Courses." Memorias del Congreso Nacional de Control Automático 7, no. 1 (2024): 422–27. https://doi.org/10.58571/cnca.amca.2024.072.
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This paper describes the creation of an educational resource focused on identifying dynamic systems, with a particular emphasis on parametric estimation, for undergraduate courses. This virtual laboratory is easily reproducible with accessible materials and software, allowing easy system modification. The Recursive Least-Squares (RLS) with a forgetting factoris a solid and efficient method that enables students to experiment with parameters and better understand the subject matter. We show three experiments to provide an exhaustive RLScomprehension. Finally, we envision the virtual laboratory as useful for educational purposes and other applications in automatic control.
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Pan, Zihao, Heng Wang, Bangning Zhang, and Daoxing Guo. "Low Complexity Adaptive Detection of Short CPM Bursts for Internet of Things in 6G." Sensors 22, no. 21 (2022): 8316. http://dx.doi.org/10.3390/s22218316.
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With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input–soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths.Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.
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Fîciu, Ionuț-Dorinel, Cristian-Lucian Stanciu, Camelia Elisei-Iliescu, and Cristian Anghel. "Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features." Electronics 11, no. 2 (2022): 237. http://dx.doi.org/10.3390/electronics11020237.
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The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.
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Silva, André, Marco Gomes, João P. Vilela, and Willie K. Harrison. "SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security." Sensors 21, no. 3 (2021): 856. http://dx.doi.org/10.3390/s21030856.
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In order to secure wireless communications, we consider the usage of physical-layer security (PLS) mechanisms (i.e., coding for secrecy mechanisms) combined with self-interference generation. We present a prototype implementation of a scrambled coding for secrecy mechanisms with interference generation by the legitimate receiver and the cancellation of the effect of self-interference (SI). Regarding the SI cancellation, four state-of-the-art algorithms were considered: Least mean square (LMS), normalized least mean square (NLMS), recursive least squares (RLS) and QR decomposition recursive least squares (QRDRLS). The prototype implementation is performed in real-world software-defined radio (SDR) devices using GNU-Radio, showing that the LMS outperforms all other algorithms considered (NLMS, RLS and QRDRLS), being the best choice to use in this situation (SI cancellation). It was also shown that it is possible to secure communication using only noise generation by the legitimate receiver, though a variation of the packet loss rate (PLR) and the bit error rate (BER) gaps is observed when moving from the fairest to an advantageous or a disadvantageous scenario. Finally, when noise generation was combined with the adapted scrambled coding for secrecy with a hidden key scheme, a noteworthy security improvement was observed resulting in an increased BER for Eve with minor interference to Bob.
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Bravo Montenegro, Diego Alberto, Carlos Felipe Rengifo, Cristian Giron, and Jhon Palechor. "Identification of a synchronous generator parameters using recursive least squares and Kalman filter." Ciencia en Desarrollo 12, no. 1 (2021): 13–21. http://dx.doi.org/10.19053/01217488.v12.n1.2021.11779.
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The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman filter is better than recursive least squares. A linear model of the machine is used in order to considerate the currents and their derivatives as the system inputs while the three-phase voltage signals are the outputs. Furthermore two experiments with simulated and measured data were carried out, three operating scenarios and two variations of the algorithms respectively were considered. Despite the great similarity and good performance of both methods, it was found that Kalman filter slightly exceeded least squares due to the fact that it presented smaller oscillations in the estimated value of the parameters for any operating condition.
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Zhang, Ming Liang, Shu Zhao Wang, and Xin Yan Jia. "RLS Adaptive Noise Cancellation via QR Decomposition for Noisy ICA." Advanced Materials Research 403-408 (November 2011): 1291–96. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1291.
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This study addresses the independent component analysis (ICA) in the presence of additive noise via an approach of adaptive filtering. Recursive least squares (RLS) adaptive noise cancellation via QR decomposition (QRRLS) is introduced to reduce the bias in the mixing matrix caused by noise. To test performance of this approach, two kinds of experiments for speech signals are conducted by combining Fast-ICA algorithm with it, on the conditions of identical noise and correlational noises respectively. Moreover, in order to measure the performance availably, the least-squares method is adopted to calculate the signal to noise ratio (SNR) of recovery signals. By comparison, it shows that this approach outperforms the adaptive noise cancellation via least-mean-squares (LMS) algorithm.
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Petrovic, Predrag. "Possible solution of parallel FIR filter structure." Serbian Journal of Electrical Engineering 2, no. 1 (2005): 21–28. http://dx.doi.org/10.2298/sjee0501021p.
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In this paper, a parallel form FIR adaptive filter structure with RLS (Recursive Least Squares) type adaptive algorithm is proposed. The proposed parallel form FIR structure consists of a recursive orthogonal transform stage and sparse FIR sub filters operating in parallel. The adaptive algorithm used to update coefficient vector of the sparse filters is implemented by using modified Hopfield networks. This structure implements the RLS-type adaptive algorithm, without an explicit matrix inversion avoiding numerical instability problems. Simulation results which show the desirable features of proposed structure are given.
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Cheng, Hui. "The Fuzzy CMAC Based on RLS Algorithm." Applied Mechanics and Materials 432 (September 2013): 478–82. http://dx.doi.org/10.4028/www.scientific.net/amm.432.478.
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In this paper, the structure of the fuzzy crebellar model articulation controller (FCMAC) neural network was discussed. The FCMAC can improve the accuracy of the CMAC. It also has excellent generalization ability and fault-tolerance ability. The recursive least squares (RLS) algorithm was introduced into the FCMAC. The FCMAC based on RLS algorithm has potential application prospect in the research of modeling and emulation on the complex systems.
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Levent, Mehmet Latif, and Omer Aydogdu. "Adaptive State Feedback Control Method Based on Recursive Least Squares." Elektronika ir Elektrotechnika 28, no. 4 (2022): 27–34. http://dx.doi.org/10.5755/j02.eie.31149.
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This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical model of the time-varying system using the input/output data packets of the controller system. Thanks to this block, the system is constantly monitored to update the parameters of the system, which change over time. Linear quadratic regulator (LQR) is renewed according to these updated parameters, and self-adjustment of the system is provided according to the changed system parameters. The Matlab/Simulink state-space model of the variable loaded servo (VLS) system module was obtained for the simulation experiments in this study; then the system was controlled. Moreover, experiments were carried out on the servo control experimental equipment of the virtual simulation laboratories (VSIMLABS). The effectiveness of the proposed new method was observed taking the performance indexes as a reference to obtain the results of the practical application of the proposed method. Regarding the analysis of simulation and experimental results, the proposed approach minimizes the load effect and noise and the system works at high efficiency.
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Gokhale, A. P., and P. M. Nawghare. "Modified recursive least squares (RLS) algorithm for neural networks using piecewise linear function." IEE Proceedings - Circuits, Devices and Systems 151, no. 6 (2004): 510. http://dx.doi.org/10.1049/ip-cds:20040614.
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Nie, Yanxin, Yiding Hua, Minglu Zhang, and Xiaojun Zhang. "Intelligent Vehicle Trajectory Tracking Control Based on VFF-RLS Road Friction Coefficient Estimation." Electronics 11, no. 19 (2022): 3119. http://dx.doi.org/10.3390/electronics11193119.
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This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance.
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Basukesti, Agus, and Bangga Dirgantara. "Algoritma Adaptif Sistem Downlink Menggunakan Recursive Least Square (RLS)." Angkasa: Jurnal Ilmiah Bidang Teknologi 9, no. 1 (2017): 1. http://dx.doi.org/10.28989/angkasa.v9i1.106.
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GPS (Global Positioning System) is the popular system for navigation which assistance 32 satellites orbiting the earth. Currently, tracking positions using the Global Positioning System (GPS) is one of the best positioning tracking methods. However, GPS has a lot o f noise, so filters are needed to handle with noise on GPS. In this research, the simulation is done to extract data from GPS sensors using RLS algorithm. From the results o f identification and simulation, it can be concluded that the algorithm works well and need to analyze the advantages and disadvantages to be implemented on the downlink system designed. From the simulation results obtained that error estimation is convergent that is the longer the smaller.
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Song, Wenxian, Guofu Wang, and Jincai Ye. "A Novel Digital Predistortion Identification Algorithm Based on Variable Forgetting Factor Recursive Least Square Method." International Journal of RF and Microwave Computer-Aided Engineering 2023 (May 20, 2023): 1–10. http://dx.doi.org/10.1155/2023/6377941.
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The transmitting signal of wireless communication system is impaired by the nonlinearity of RF power amplifier (PA) which leads to signal distortion and spectrum spillover, by which the signal transmission quality is affected. Digital predistortion (DPD) is an efficient and economical way to correct the nonlinear effects of power amplifiers. The recursive least square (RLS) recognition algorithm is commonly used to extract the correction coefficients of the DPD model, and the accuracy of the extraction directly affects the system performance. In this paper, a new variable forgetting factor identification algorithm (new variable forgetting factor recursive least square, NVFFRLS) is proposed for recursive least square (RLS) identification algorithm. The 64-QAM signal is combined with a memory polynomial (MP) predistortion model for predistortion system simulation. The experimental results show that, compared with the RLS identification algorithm and two kinds of variable forgetting factor RLS identification algorithms, the algorithm has smaller estimation error, faster convergence, and better tracking capability, stability, and adaptability; the predistortion system based on NVFFRLS identification algorithm can compensate the nonlinear memory effects of power amplifier more effectively.
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Gao, Chunhua, Yanping Yang, Mengyuan Qin, Cun Li, and Zihan Yuan. "Research on parameter identification of shaking table systems based on the RLS method." PLOS ONE 17, no. 12 (2022): e0279092. http://dx.doi.org/10.1371/journal.pone.0279092.
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It is difficult to accurately establish a model of the real mesa system. Furthermore, a model of a seismic simulation vibration table array system is critical to increasing the accuracy of seismic testing in laboratory settings. Herein a model of the nine subarray shaking table system is identified by recursive extension of the least square method, which is used to accurately identify the structure parameters by simulation of the structure assuming a single degree-of-freedom. Then, through the displacement of the empty shaking table and the application of the recursive least squares algorithm, the model of the seismic simulation vibration table array is established. Through this study, the vibration table model of different construction forms can be obtained, and the parameters that are difficult to measure for some complex structures can effectively be determined.