• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.97-105
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• 2007

We consider a nonparametric additive risk model that is based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huller and Mckeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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• pp.49-50
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• 2006

We consider a nonparametric additive risk model that are based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• 제어로봇시스템학회논문지
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• 제13권7호
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• 전기학회논문지
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• 제66권4호
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권1호
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• pp.1-22
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• 2020

An accurate and efficient gradient estimation method on unstructured grid is presented by proposing a switching process between two Least-Square methods. Diverse test cases show that the gradient estimation by Least-Square methods exhibit better characteristics compared to Green-Gauss approach. Based on the investigation, switching between the two Least-Square methods, whose merit complements each other, is pursued. The condition number of the Least-Square matrix is adopted as the switching criterion, because it shows clear correlation with the gradient error, and it can be easily calculated from the geometric information of the grid. To illustrate switching process on general grid, condition number is analyzed using stencil vectors and trigonometric relations. Then, the threshold of switching criterion is established. Finally, the capability of Switching Weighted Least-Square method is demonstrated through various two- and three-dimensional applications.

• 전기학회논문지
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• 제64권1호
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Structural Engineering and Mechanics
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• 제34권1호
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• 전자공학회논문지SC
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• 제44권4호통권316호
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• pp.1-7
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• 2007

지상파 측위와 같이 사용자와 센서간의 거리가 짧은 경우에는 GN (Gauss-Newton) 방법을 이용하여 구한 추정치가 발산하기도 한다. 이러한 문제점을 해결하기 위하여 TDOA (Time Difference of Arrival) 측정치에 대해서는 QCLS (Quadratic Correction Least Square) 방법이 개발되었으나 추정치의 오차가 다소 크다는 문제점을 보였다. 본 논문에서는 가중최소자승법을 도입하여 기존 QCLS 방법의 성능을 개선하는 방안을 제안하였다. 제안한 방법에서 사용하는 가중행렬이 미지변수인 사용자 위치의 함수이기 때문에 먼저 가중행렬의 추정치를 구한 후 이를 이용하여 사용자 위치 추정치를 구하는 단계별 추정 방식을 제안하였다. 컴퓨터 시뮬레이션을 통하여 제안한 방법의 성능이 기존 QCLS 방법보다 항상 우수함을 보였으며, Gauss-Newton 방법이 수렴하는 경우 두 가지 방법이 대등한 성능을 보였다.

• 응용통계연구
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• 제29권4호
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• pp.595-611
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• 2016

일반적으로 독성학 또는 약리학에서는 자료를 분석할 때 Hill Model과 같은 비선형 회귀모형을 사용한다. 비선형 회귀모형에서 모수의 추정량과 그것의 불확실성(uncertainty)에 대한 측도의 추정은 오차의 분산 구조에 영향을 받게 된다. 따라서 자료가 등분산인지 혹은 이분산인지에 따라 사용하여야 할 추정 방법이 달라져야 한다. 그러나 일반적으로 자료를 실제로 분석하기 전에는 오차의 분산구조에 대해서 잘 알 수 없다. 그러므로 오차의 분산구조에 로버스트한 추정 방법을 개발하는 것은 중요한 문제이다. 본 논문에서는 예비검정 방법을 기반으로 한 비선형 회귀모형에서의 모수 추정 방법을 제안하였다. 오차 분산의 등분산성에 대한 간단한 예비검정의 결과에 따라 보통 최소제곱 추정(ordinary Least Square Estimation) 방법과 반복 가중 최소제곱 추정(iterative weighted least square estimation) 방법을 사용하는 추정량을 정의하였다. 제안된 추정량은 모의실험 연구를 통하여 기존의 표준적인 추정량들과 그 성능을 비교하였다. 또한 미국의 National Toxicology Program으로부터 얻어진 실제자료를 사용하여 추정 방법들을 비교하였다.