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Compressed least squares Algorithm of Continuous-Time Linear Stochastic Regression Model Using Sampling Data

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Journal of Systems Science & Complexity 2024年第4期37卷 1488-1506页

作者： XIE Siyu ZHANG Shujun WANG Ziming GAN Die School of Aeronautics and Astronautics University of Electronic Science and Technology of ChinaChengdu 611731China China Academy of Aerospace Science and Innovation Beijing 100176China Zhongguancun Laboratory Beijing 100094China

In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling *** on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter *** each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown ***,the original high-dimensional sparse unknown parameter is recovered by a reconstruction *** introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system *** last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.

关键词： Compressed excitation condition compressed sensing continuous-time model least squares linear stochastic regression parameter identification sampling data

least squares based iterative estimation for multi-input output-error systems using the data filtering 36

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Least squares based iterative estimation for multi-input out...

Classification using least squares support vector machine for reliability analysis

第36届中国控制会议

作者： Jiling Ding Huibo Chen Department of Mathematics Jining University School of Electrical Engineering Sanmenxia Polytechnic

ISBN: (纸本)9781538629185

This paper considers the parameter estimation for multi-input output error autoregressive（OEAR） systems. The main contribution of this paper is to propose iterative algorithms for multi-input OEAR systems using the data filtering ***, a least squares iterative（LSI） identification algorithm is given for multi-input OEAR systems as a comparison. Secondly,we transform an OEAR system into two identification models and present a data filtering based least squares iterative（F-LSI）identification algorithm. Finally, an illustrative simulation example is provided to test the proposed algorithms, the simulation results indicate that the F-LSI identification algorithm can produce more accurate parameter estimates than the LSI identification algorithm.

关键词： Multivariable system System identification least squares Iterative algorithm Data filtering Multi-input system

来源： cnki会议评论

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Applied Mathematics and Mechanics(English Edition) 2009年第7期30卷 853-864页

作者：郭秩维白广忱 School of Jet Propulsion Beijing University of Aeronautics and Astronautics

In order to improve the efficiency of the support vector machine （SVM） for classification to deal with a large amount of samples, the least squares support vector machine （LSSVM） for classification methods is introduced into the reliability analysis. To reduce the coraputational cost, the solution of the SVM is transformed from a quadratic programming to a group of linear equations. The numerical results indicate that the reliability method based on the LSSVM for classification has higher accuracy and requires less computational cost than the SVM method.

关键词： least squares support vector machine classification reliability performancefunction

Interval analysis using least squares support vector fuzzy regression

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控制理论与应用（英文版） 2012年第4期10卷 458-464页

作者： Yongqi CHEN Qijun CHEN College of Science and Technology Ningbo University Department of Control Science and Engineering Tongji University

A least squares support vector fuzzy regression model （LS-SVFR） is proposed to estimate uncertain and imprecise data by applying the fuzzy set principle to weight vectors. This model only requires a set of linear equations to obtain the weight vector and the bias term, which is different from the solution of a complicated quadratic programming problem in existing support vector fuzzy regression models. Besides, the proposed LS-SVFR is a model-free method in which the underlying model function doesn＇t need to be predefined. Numerical examples and fault detection application are applied to demonstrate the effectiveness and applicability of the proposed model.

关键词： Interval analysis least squares Fuzzy regression Fuzzy sets

Simultaneous least squares f itter based on the Lagrange multiplier method

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Chinese Physics C 2013年第10期37卷 63-67页

作者：管颖慧吕晓睿郑阳恒朱永生 University of Chinese Academy of Sciences Institute of High Energy Physics Chinese Academy of Sciences

We developed a least squares fitter used for extracting expected physics parameters from the correlated experimental data in high energy physics. This fitter considers the correlations among the observables and handles the nonlinearity using linearization during the X2 minimization. This method can naturally be extended to the analysis with external inputs. By incorporating with Lagrange multipliers, the fitter includes constraints among the measured observables and the parameters of interest. We applied this fitter to the study of the D^-D~ mixing parameters as the test-bed based on MC simulation. The test results show that the fitter gives unbiased estimators with correct uncertainties and the approach is credible.

关键词： least squares correlated uncertainties nonlinearity constrained fit

Improved Scheme for Fast Approximation to least squares Support Vector Regression

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Transactions of Nanjing University of Aeronautics and Astronautics 2014年第4期31卷 413-419页

作者：张宇宸赵永平宋成俊侯宽新脱金奎叶小军 School of Mechanical Engineering Nanjing University of Science and Technology Military Ammunition in Shenyang Representative Office Civil Aviation Flight University of China Heilongjiang North Tool Company Limited New Star Research Institute of Applied Technology

The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain *** overcome this obstacle,a scheme,named I2FSA-LSSVR,is *** with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting *** a result,I2FSA-LSSVR reduces the number of support vectors and enhances the *** confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.

关键词： support vector regression kernel method least squares sparseness

Thrust estimator design based on least squares support vector regression machine

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Journal of Harbin Institute of Technology(New Series) 2010年第4期17卷 578-583页

作者：赵永平孙健国 Intelligent Ammunition Tecnology Laboratory of National Defence Nanjing University of Science & Technology College of Energy and Power Engineering Nanjing University of Aeronautics and Astronautics

In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.

关键词： least squares support vector machine direct thrust control wrapper criterion

The structured total least squares algorithm research for passive location based on angle information

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Science in China(Series F) 2009年第6期52卷 1043-1054页

作者： WANG Ding ZHANG Li WU Ying Institute of Information Engineering PLA Information Engineering University Zhengzhou 450002 China

Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） *** solution of the STLS problem for passive location can be obtained using the inverse iteration *** also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain ***, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.

关键词： passive location angle information least squares total least squares constrained total least squares structured total least squares Kalman filter singular value decomposition QR decomposition

FINITE ELEMENT SOLUTION OF MULTI-SCALE TRANSPORT PROBLEMS USING THE least squares-BASED BUBBLE FUNCTION ENRICHMENT

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International Journal of Modeling, Simulation, and Scientific Computing 2012年第4期3卷 115-131页

作者： A.YAZDANI V.NASSEHI Department of Civil and Environmental Engineering Rice University Houston TX 77005USA Department of Chemical Engineering Loughborough University Loughborough LE113TUUK

This paper presents a technique for deriving least-squares-based polynomial bubble functions to enrich the standard linear finite elements,employed in the formulation of Galerkin weighted-residual *** element-level linear shape functions are enhanced using supplementary polynomial bubble functions with undetermined *** enhanced shape functions are inserted into the model equation and the residual functional is constructed and minimized by using the method of the least squares,resulting in an algebraic system of equations which can be solved to determine the unknown polynomial coefficients in terms of element-level nodal *** stiffness matrices are subsequently formed with the standard finite elements assembly procedures followed by using these enriched elements which require no additional nodes to be introduced and no extra degree of freedom ***,the proposed technique is tested on a number of benchmark linear transport equations where the quadratic and cubic bubble functions are derived and the numerical results are compared against the exact and standard linear element *** is demonstrated that low order bubble enriched elements provide more accurate approximations for the exact analytical solutions than the standard linear elements at no extra computational cost in spite of using relatively crude *** the other hand,it is observed that a satisfactory solution of the strongly convection-dominated transport problems may require element enrichment by using significantly higher order polynomial bubble functions in addition to the use of extremely fine computational meshes.

关键词： Bubble function finite elements least squares reaction-diffusion transport equation Galerkin method.