• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.227-230
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• 1996

The paper highlights the need for cautious least squares estimation when dealing with industrial applications of bilinear self-tuning control and indicates in qualitative terms the benefits of the approach over linear self-tuning control schemes. The cautious least squares algorithm is described and the use of cautious self-tuning in the context of both commissioning and implementation discussed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.625-628
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• 1997

In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(1)
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• pp.45-48
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• 2000

The use of multi-level modulation scheme in the wireless LAN(Local Area Networks) system requires an accurate channel estimation. In this paper, we present sequential least squares(LS) channel estimation scheme based on decision-directed channel tracking scheme. The proposed scheme improves the performance of the conventional LS estimator for wireless LAN. In addition, its structure is suitable for the high-rate wireless LAN. Simulation results show that the proposed scheme achieves about IdB Packet Error Rate(PER) gain compared to the LS scheme in a frequency selective channel.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.167-170
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• 1998

본 논문에서는 주파수 영역에서 구현되는 안테나 배열을 이용한 적응형 등화기의 구조를 제안하였다. 제안된 방식은 주파수 영역에서 블록 LMS (least mean squares) 방식을 사용하기 때문에 수렴 속도가 빠르고 계산량이 적다. 또한 안테나 배열을 사용함으로서 다중경로 환경에서 한 개의 안테나를 사용한 경우에 비해 특히 우수한 성능을 지닌다. 다중 경로를 통한 신호들을 분리하기 위해 학습신호를 사용하여 반복적으로 출력신호와의 오차를 최소화하는 방법을 사용하였다. 시뮬레이션을 통하여 안테나의 개수, 신호의 입사각, 세기 등에 따른 성능을 분석하고, 제안된 방식이 다이버시티 시스템에 사용되는 경우에 대하여도 성능을 분석하였다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.337-343
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• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.183-193
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• 2007

This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

• Journal of the Korean Data and Information Science Society
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• 제8권2호
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• pp.127-134
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• 1997

A method is proposed for the choice of the number of principal components in principal component regression based on the predicted error sum of squares. To do this, we approximately evaluate that statistic using a linear approximation based on the perturbation expansion. In this paper, we apply the proposed method to various data sets and discuss some properties in choosing the number of principal components in principal component regression.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• Journal of applied mathematics & informatics
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• 제4권2호
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• pp.387-396
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• 1997

For given discrete derivative data in a rectangular re-gion we propose a method to generate an approximated surface which fits the given derivative data in the region and extends smoothly to a sufficiently large rectangular region. Such an extension in nec-essary in the generation of the surface in NC(numerical control) ma-chine.

• Journal of applied mathematics & informatics
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• 제32권5_6호
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• pp.761-781
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• 2014

An exponentially accurate nonconforming spectral element method for elasticity systems with discontinuities in the coefficients and the flux across the interface is proposed in this paper. The method is least-squares spectral element method. The jump in the flux across the interface is incorporated (in appropriate Sobolev norm) in the functional to be minimized. The interface is resolved exactly using blending elements. The solution is obtained by the preconditioned conjugate gradient method. The numerical solution for different examples with discontinuous coefficients and non-homogeneous jump in the flux across the interface are presented to show the efficiency of the proposed method.