• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.2
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• pp.159-163
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• 2006

In order to tune the speed controller in the linear servo applications an accurate information of a mover mass including a load mass is always required. This paper suggests the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) 4y using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. Then the application of the Least-Squares algorithm shows that the mass can be accurately estimated both in the simulation results and in the experimental results.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2344-2353
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• 2018

A coupled recursive total least squares (CRTLS) algorithm is proposed for parameter estimation of permanent magnet synchronous machines (PMSMs). TLS considers the errors of both input variables and output ones, and thus achieves more accurate estimates than standard least squares method does. The proposed algorithm consists of two recursive total least squares (RTLS) algorithms for the d-axis subsystem and q-axis subsystem respectively. The incremental singular value decomposition (SVD) for the RTLS obtained by an approximate calculation with less computation. The performance of the CRTLS is demonstrated by simulation and experimental results.

• Journal of the Korean Mathematical Society
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• v.54 no.2
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• pp.613-626
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• 2017

For the overdetermined linear system, when both the data matrix and the observed data are contaminated by noise, Total Least Squares method is an appropriate approach. Since an ill-conditioned data matrix with noise causes a large perturbation in the solution, some kind of regularization technique is required to filter out such noise. In this paper, we consider a Dual regularized Total Least Squares problem. Unlike the Tikhonov regularization which constrains the size of the solution, a Dual regularized Total Least Squares problem considers two constraints; one constrains the size of the error in the data matrix, the other constrains the size of the error in the observed data. Our method derives two nonlinear equations to construct the iterative method. However, since the Jacobian matrix of two nonlinear equations is not guaranteed to be nonsingular, we adopt a trust-region based iteration method to obtain the solution.

• ETRI Journal
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• v.42 no.6
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• pp.922-931
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• 2020

A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS-based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.3
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• pp.312-319
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• 2011

In this paper, a new linearization algorithm for DPD(Digital PreDistorter) is suggested. This new algorithm uses DFP(Davidon-Fletcher-Powell) method. This algorithm is more accurate than that of the existing algorithms, and this method renew the best-fit value in every routine with out setting the initial value of step-size. In modeling power amplifier, the memory polynomial model which can model the memory effect of the power amplifier is used. And the overall structure of linearizer is based on an indirect learning architecture. In order to verify for performance of proposed algorithm, we compared with LMS(Least Mean-Squares), RLS(Recursive Least squares) algorithm.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.1-12
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• 2009

Iterative algorithms are proposed for the least-squares symmetric solution of AXB = E with a submatrix constraint. We characterize the linear mappings from their independent element space to the constrained solution sets, study their properties and use these properties to propose two matrix iterative algorithms that can find the minimum and quasi-minimum norm solution based on the classical LSQR algorithm for solving the unconstrained LS problem. Numerical results are provided that show the efficiency of the proposed methods.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.159-161
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• 2003

Fitting the projected wire-frame model to the detected edge pixels on images by using least-squares approach, called Least-squares Model-image Fitting (LSMIF), is the key of the Model-based Building Extraction (MBBE). It is implemented by iteratively adjusting the model parameters to minimize the squares sum of distances from the extracted edge pixels to the projected wire-frame. This paper describes a series of experiments and studies on various factors affect the fitting results, including the edge detectors, the weighting rules, the initial value of parameters, and the number of overlapped images. The experimental result is not only helpful to clarify the influences of each factor, but is also able to enhance the robustness of the LSMIF algorithm.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.97-101
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• 2010

FIR type system identification with noisy input and output data can be solved by a total least squares (TLS) estimation. However, the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose an iterative convex combination algorithm between TLS and least squares (LS). This combined algorithm shows robustness against the noise variance ratio. Consequently, the practical workability of the TLS method with noisy data has been significantly broadened.