• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.373-385
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• 2007

The solution of the normal equation arising in a general linear model by the least square methods is not unique in general. Conventionally, SAS IML and G-inverse matrices are considered for such problems. In this paper, we provide a systematic solution procedures for SAS IML.

• Journal of KSNVE
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• v.7 no.5
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• pp.773-780
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• 1997

Using Generalized Inverse Method presented by Udwadia and Kalaba in 1992, we can obtain equations to exactly describe the motion of constrained systems. When the differential equations are numerically integrated by any numerical integration scheme, the numerical results are generally found to veer away from satisfying constraint equations. Thus, this paper deals with the numerical integration of the differential equations describing constrained systems. Based on Baumgarte method, we propose numerical methods for reducing the errors in the satisfaction of the constraints.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.7A
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• pp.1250-1256
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• 2001

개선된 리버스 자켓 행렬(Reverse Jacket matrix)의 정의와 함께 그의 역행렬을 소개한다. 새로이 정의된 리버스 자켓 행렬은 실베스터 타입의 하다마드 행렬을 이용하여 더욱 일반화되었다. 이 논문에서는 고속 리버스 자켓 변환(fast Reverse Jacket transform)을 제시하며 또한 이 알고리즘이 4점 이산 푸리에 변환으로 응용이 됨을 보여준다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.37-48
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• 1995

This paper presents an sstimation of ARMA coefficients of noisy ARMA system using generalized total least square (GTLS) method. GTLS problem for ARMA system is defined as minimizing the errors between the noisy output vectors and estimated noisy-free output. The GTLS problem is solved in closed form by eigen-problem and the perturbation analysis of GTLS is presented. Also its recursive solution (recursive GTLS) is proposed using the power method and the covariance formula of the projected output error vector into the input vector space. The simulation results show that GTLS ARMA coefficients estimator is an unbiased estimator and that recursive GTLS achieves fast convergence.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.11-18
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• 2009

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix. the Levenberg-Marquardt learning must increase or decrease ${\mu}$ and recalculate the inverse matrix of the Jacobian matrix due to these changes of ${\mu}$. Therefore, to have the proper ${\mu}$, we create the principal submatrix of Jacobian matrix and set the ${\mu}$ as the eigenvalues sum of the principal submatrix. which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.154-162
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• 1997

본 논문은 구속된 기계 시스템의 운동 제어 설계를 위한 새로운 방법을 제안한다. 구속된 기계 시스템의 운동 제어에는 지금까지 주로 사용되어온 Lagrange의 운동 방정식에 의한 모델링 보다 Udwadia와 Kalaba에 의해 제안된 운동 방정식에 의한 모델링이 더욱 적합함을 보였으며 이는 Holonomic 및 Nonholonomic 구속 조건을 비롯한 대부분의 구속 조건이 포함된다. 문헌에 잘 알려진 두 시스템을 시뮬레이션을 통하여 비교 함으로써 본 논문에 제안된 방법이 보다 우수한 결과를 보여줌을 확인 할 수 있었다. 또한 지금까지 불가능 하였던 비선형 일반 속도(gereralized velocity)를 포함한 구속 조건도 용이하게 제어됨을 보임으로써 광범위한 구속된 기계 시스템의 제어 문제를 통일된 방법으로 접근 할 수 있음을 제시하였다.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.127-132
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• 1998

Mobile wheeled robots are nonholonomically constrained systems. Generally, it is very difficult to describe the motion of mechanical systems with nonintegrable nonholonomic constraints. An objective of this study is to describe the motion of a robot with a free wheel. The motion of holonomically and/or nonholonomically constrained system can be simply determined by Generalized Inverse Method presented by Udwadia and Kalaba in 1992. Using the method, we describe the exact motion of the robot and determine the constraint force exerted on the robot for satisfying constraints imposed on it. The application illustrates the ease with which the Generalized Inverse Method can be utilized for the purpose of control of nonlinear system without depending on any linearization, maintaining precision tracking motion and explicit determination of control forces of nonholonomically constrained system.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.333-342
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• 2008

Variable selection theorem in the linear regression model is extended to the analysis of covariance model. When some of regression variables are omitted from the model, it reduces the variance of the estimators but introduces bias. Thus an appropriate balance between a biased model and one with large variances is recommended.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.39-49
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• 2015

Some characters and construction theorems of Pseudo Jacket Matrix which is generalized from Jacket Matrix introduced by Jacket Matrices: Construction and Its Application for Fast Cooperative Wireless signal Processing[27] was announced. In this paper, we proposed some examples of Pseudo inverse Jacket matrix, such as $2{\times}4$, $3{\times}6$ non-square matrix for the MIMO channel. Furthermore we derived MIMO singular value decomposition (SVD) pseudo inverse channel and developed application to utilize SVD based on channel estimation of partitioned antenna arrays. This can be also used in MIMO channel and eigen value decomposition (EVD).