• 대한전기학회논문지
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• 제33권9호
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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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• 제7권2호
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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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• 제28권2호
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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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• 제25권3호
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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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• 제28권1호
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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.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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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.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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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.

• Journal of the Korean Statistical Society
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• 제33권1호
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• pp.25-34
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• 2004

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.3-6
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• 2004

In this paper, the problem of identifying a time-varying nonlinear system in an adaptive way was considered, whereby the time-varying second-order Volterra series was employed to model the system and the filtered weighted least squares (FWLS) algorithm was utilized for the fast parameter tracking capability with low computational burden. Finally, the performance of the proposed approach was demonstrated by providing some computer simulation results.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.539-544
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• 2005

In this paper, we propose the one-step least squares method based on the squared differences to estimate the parameters of the variogram used for spatial data modelling, and discuss its asymptotic efficiency. The proposed method does not require to specify lags of interest and partition lags, so that we can delete the subjectiveness and ambiguity originated from the lag selection in estimating spatial dependence.