• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.51-64
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• 1999

We give a new characterization of the solutions set of the general (inconsistent) linear least-squares problem using the set of linit-points of an extended version of the classical Daczmarz's pro-jections method. We also obtain a "step error reduction formula" which in some cases can give us apriori information about the con-vergence properties of the algorithm. Some numerical experiments with our algorithm and comparisons between it and others existent in the literature are made in the last section of the paper.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.9
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• pp.372-376
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• 1984

This paper discusses on the stochastic adaprive control which uses a least squares method in the parameter adaptation law. Especially we study on the reason why existing methods have adopted modified least squares methods. After examining the performances of these methods for time-varying systems, we propose a new method to deal with such a situation, study on the stability problem, and finally show the effectiveness of the method with a computer simulation example.

• Korean Journal of Mathematics
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• v.7 no.2
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• pp.245-269
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• 1999

Multigrid method and acceleration by conjugate gradient method for first-order system least squares (FOSLS) using bilinear finite elements are developed for various boundary value problems of planar linear elasticity. They are two-stage algorithms that first solve for the displacement flux variable, then for the displacement itself. This paper focuses on solving for the displacement flux variable only. Numerical results show that the convergence is uniform even as the material becomes nearly incompressible. Computations for convergence factors and discretization errors are included. Heuristic arguments to improve the convergences are discussed as well.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.183-193
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• 1999

In the problem of estimating estimable functions in classification linear models, we propose a method of obtaining least squares estimators of estimable functions. This method is based on the hierarchical Bayesian approach for estimating a vector of unknown parameters. Also, we verify that estimators obtained by our method are identical to least squares estimators of estimable functions obtained by using either generalized inverses or full rank reparametrization of the models. Some examples are given which illustrate our results.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.369-379
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• 1996

A study is made of approximate technique for structural reanalysis based on the force method. Perturbntion analysis of generalized least squares problem is adopted to reanalyze a damaged structure, and related results are presented.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.45-55
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• 1999

Knowing the number of unit roots is important in the analysis of k-dimensional multivariate autoregressive process. In this paper we suggest simple multiple unit roots test statistics based on least squares estimator for the multivariate AR(1) process in which some eigenvalues are one and the rest are less than one in magnitude. The empirical distributions are tabulated for suggested test statistics. We have small Monte-Calro studies to compare the powers of the test statistics suggested by Johansen(1988) and in this paper.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.23-27
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• 1991

RLS(Recursive Least Squares)나 LMS(Least mean square)등은 알고리듬 고유의 성질상 잡음이 섞인 시스템에 있어서는 올바른 역 모델링을 할 수 없다. 따라서, 잡음의 영향을 받지않는 견실한(robust) 모델 추정 알고리듬이 필요하다. 본 논문에서는 잡음환경하에 있는 시스템을역 모델링하는데 있어서, 잡음의 영향을 줄이기위해 완전최소자승법을 도입하고 기존의 최소자승법과 비교 실험하였다. 그리고, 이 방법의 적응 알고리듬을 제안하였으며, RLS(Recursive least squares)와 그 성능을 비교하여 타당성을 검토하였다.

• 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 KIPE Conference
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• 2005.07a
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• pp.427-429
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• 2005

In order to tune the speed controller in the linear servo applications the 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) by using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. 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.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2E
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• pp.63-68
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• 2007

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, we applied this neural network model to channel equalization. Simulations show that the neural network based DLS outperforms ordinary least squares in channel equalization problems.