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recursive least-squares algorithm REVISITED

IEE PROCEEDINGS-D CONTROL THEORY AND APPLICATIONS 1981年第2期128卷 74-76页

作者： FUCHS, JJJ Laboratoire d'Automatique Institut de Recherche en Informatique et Systèmes Aléatoires Rennes France

A modified form of the recursive least-squares algorithm is proposed. It is shown that this algorithm possesses interesting properties that are valid without any restriction on experimental conditions, or stability as... 详细信息

关键词： Self-adjusting control systems Simulation, modelling and identification least squares approximations recursive least-squares algorithm Interpolation and function approximation (numerical analysis) parameter estimation adaptive control

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A Fast Robust recursive least-squares algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第3期57卷 1209-1216页

作者： Rey Vega, Leonardo Rey, Hernan Benesty, Jacob Tressens, Sara Univ Buenos Aires CONICET RA-1063 Buenos Aires DF Argentina Univ Buenos Aires Dept Elect RA-1063 Buenos Aires DF Argentina Inst Ingn Biomed FIUBA RA-1063 Buenos Aires DF Argentina Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada

We present a fast robust recursive least-squares (FRRLS) algorithm based on a recently introduced new framework for designing robust adaptive filters. The algorithm is the result of minimizing a cost function subject to a time-dependent constraint on the norm of the filter update. Although the characteristics of the exact solution to this problem are known, there is no closed-form solution in general. However, the approximate solution we propose is very close to the optimal one. We also present some theoretical results regarding the asymptotic behavior of the algorithm. The FRRLS is then tested in different environment's for system identification and acoustic echo cancellation applications.

关键词： Acoustic echo cancellation impulsive noise recursive least-squares algorithm robust filtering system identification

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Unsupervised robust recursive least-squares algorithm for impulsive noise filtering

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Science China(Information Sciences) 2013年第4期56卷 203-212页

作者： CHEN Jie MA Tao CHEN WenJie PENG ZhiHong School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision(Beijing Institute of Technology) Ministry of Education

A robust recursive least-squares(RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired *** minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function,a possible impulse-corrupted signal is prevented from entering the filter’s weight updating ***,a multi-step adaptive filter is devised to reconstruct the observed "impulse-free" noisy sequence,and whenever impulsive noise is detected,the impulse contaminated samples are replaced by predictive *** on simulation and experimental results,the proposed unsupervised robust recursive least-square adaptive filter performs as well as conventional RLS filters in "impulse-free" circumstances,and is effective in restricting large disturbances such as impulsive noise when the RLS and the more recent unsupervised adaptive filter fails.

关键词： unsupervised adaptive filtering impulsive noise suppression recursive least-squares algorithm

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A general fuzzified CMAC based reinforcement learning control for ship steering using recursive least-squares algorithm

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NEUROCOMPUTING 2010年第4-6期73卷 700-706页

作者： Shen, Zhipeng Guo, Chen Zhang, Ning Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China

A general fuzzified cerebellar model articulation controller (GFCMAC) is proposed. The mapping of receptive field functions, the selection law of membership function and the learning algorithm are presented. recursive least-squares temporal difference algorithm (RLS-TD) is deduced, which can use data efficiently with faster convergence and less computational burden. Using RLS-TD method a reinforcement learning structure based on GFCMAC is applied to ship steering control, as provides an efficient way for the improvement of ship steering control performance. The parameters of controller are online learned and adjusted. Simulation results show that the ship course can be properly controlled in case of the disturbances of wave and wind. It is demonstrated that the proposed algorithm is a promising alternative to conventional autopilots. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Cerebellar model articulation controller recursive least-squares algorithm Reinforcement learning Ship steering Temporal difference

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New adaptive robust Hc control of smart structures using synchrosqueezed wavelet transform and recursive least-squares algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 116卷

作者： Li, Zhijun Adeli, Hojjat Xian Technol Univ Sch Civil & Architecture Engn Xian 710032 Peoples R China Ohio State Univ Columbus OH 43220 USA

Two kinds of uncertainties, one due to the dynamic earthquake loads with a wide frequency band and the other due to structural parameters exist in large and complex real-life structures. Most existing control algorithms consider only one of them, resulting in difficulty to guarantee necessary control performance for large complex structures, such as better vibration suppression on structural peak response and robustness performance. Con-sidering the two uncertainties simultaneously, in this paper, a new adaptive robust Hc control methodology is presented for vibration control of structures through adroit integration of synchrosqueezed wavelet transform (SWT) and recursive least-squares (RLS) algorithm. The robust Hc control is more effective than the traditional LQR/LQG control in terms of the stability and robustness of the control system. The external excitation signal from ground sensors is filtered by a low-pass filter based on SWT and then inputted into the filtered-x RLS adaptive controller. The effectiveness, accuracy, and computational efficiency of the new adaptive control method is demonstrated using a 76-story wind-excited benchmark super high-rise building structure and a 24-story shear-wall building with an active tuned mass damper (ATMD) system on the top floor. Compared with the existing linear quadratic Gaussian control algorithm, the wavelet-hybrid feedback-least mean square algorithm and the robust Hc control algorithm, the simulation results show that the control effect and robust performance indexes of the structure are increased by 5%-35% and 5%-25%, respectively, using the new control methodology.

关键词： Adaptive hybrid feedback control RobustHc control Synchrosqueezed wavelet transform recursive least-squares algorithm

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A comparative study of three recursive least-squares algorithms for single-tone frequency tracking

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SIGNAL PROCESSING 2003年第9期83卷 2059-2062页

作者： So, HC City Univ Hong Kong Dept Comp Engn & Informat Technol Kowloon Hong Kong Peoples R China

Modified covariance, Pisarenko harmonic decomposition and reformed Pisarenko harmonic decomposition methods are three closed form frequency estimators, which are derived from the linear prediction property of sinusoidal signals. In this paper, we develop the recursive least-squares-type realizations of these estimators for a single real tone, and their frequency tracking performances are contrasted via computer simulations. (C) 2003 Elsevier B.V. All rights reserved.

关键词： adaptive frequency tracking real sinusoid recursive least-squares algorithm

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Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms

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SIGNAL PROCESSING 2024年 225卷

作者： Patel, Vinal Bhattacharjee, Sankha Subhra Jensen, Jesper Rindom Christensen, Graesboll Benesty, Jacob ABV IIITM Dept Elect & Elect Engn Gwalior 474015 Madhya Pradesh India Aalborg Univ Dept Elect Syst Aalborg Denmark Univ Quebec INRS EMT Montreal PQ Canada

Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.

关键词： Nonlinear echo cancellation Low-rank approximation Nearest Kronecker product recursive least-squares algorithm

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Efficient recursive least-squares algorithms for the identification of bilinear forms

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DIGITAL SIGNAL PROCESSING 2018年 83卷 280-296页

作者： Elisei-Iliescu, Camelia Stanciu, Cristian Paleologu, Constantin Benesty, Jacob Anghel, Cristian Ciochina, Silviu Univ Politehn Bucuresti 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Politehn Bucuresti Telecommun Dept 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Politehn Bucuresti Telecommun Dept Fac Elect Telecommun & Informat Technol 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada

Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering, including system identification scenarios. However, the computational complexity of this algorithm represents a major limitation in applications that involve long filters. Moreover, when the parameter space becomes large, the system identification problem is more challenging and the adaptive filters should be able to cope with this aspect. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. From this perspective, the solution requires a multidimensional adaptive filtering technique. Recently, the RLS algorithm tailored for bilinear forms (namely RLS-BF) was developed for this purpose. In this framework, the contribution of this paper is mainly twofold. First, in order to reduce the computational complexity of the RLS-BF algorithm, two versions based on the dichotomous coordinate descent (DCD) method are proposed;due to its arithmetic features, the DCD algorithm represents one of the most attractive alternatives to solve the normal equations. However, in the bilinear context, we need to consider the particular structure of the input data and the additional related challenges. Second, in order to improve the robustness of the RLS-BF algorithm in noisy environments, a regularized version is developed, together with a method to find the regularization parameters, which are related to the signal-to-noise ratio (SNR). Furthermore, using a proper estimation of the SNR, a variable-regularized RLS-BF algorithm is designed and two DCD-based low-complexity versions are proposed. Due to their nature, these variable-regularized algorithms have good robustness features against additive noise, which make them behave well in different noisy condition scenarios. Simulation results indicate the good performance of the proposed low-comple

关键词： Adaptive filter Bilinear forms recursive least-squares algorithm Dichotomous coordinate descent Variable regularization System identification

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Regularized recursive least-squares algorithms for the Identification of Bilinear Forms 13

Regularized Recursive Least-Squares Algorithms for the Ident...

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13th International Symposium on Electronics and Telecommunications (ISETC)

作者： Elisei-Iliescu, Camelia Paleologu, Constantin Stanciu, Cristian Anghel, Cristian Ciochina, Silviu Benesty, Jacob Univ Politehn Bucuresti Bucharest Romania Univ Quebec INRS EMT Montreal PQ Canada

ISBN: (纸本)9781538659250

The system identification problem is more challenging when the parameter space becomes large. This paper addresses the identification of bilinear systems based on the regularized recursive least-squares algorithm. Here, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.

关键词： Adaptive filter bilinear forms recursive least-squares algorithm variable regularization system identification

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Road grade estimation based on the curvature of the vertical profile of a road

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2017年第4期231卷 476-486页

作者： Lin, Nan Shi, Shuming Jilin Univ Transportat Coll 5988 Renmin St Changchun 130025 Peoples R China

A road grade estimation model which uses the curvature to express the rate of change in the grade is proposed in this paper. The assumption that the rate of change in the road grade equals zero is widely accepted in the field of online road grade estimation. This assumption is reasonable to some extent, but it results in an inevitable time lag in the rolling-hills situation. This paper offers a road curvature estimation method which can be used to express the rate of change in the road grade. The recursive least-squares algorithm is used to the estimate the curvature, and then the Kalman filter is employed to estimate the road grade from the other vehicle states. Field tests are performed on a highway in a mountainous area. The offline road grade is used to analyse the instantaneous error and the time lag. The field test results show that the model performs well in reducing the time lag, especially in periods where the gradient changes rapidly.

关键词： Road grade estimation curvature estimation recursive least-squares algorithm Kalman filter

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