• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 춘계학술대회
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• pp.91-101
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• 2004

The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• 한국지반공학회논문집
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• 제39권4호
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• pp.45-54
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• 2023

설계 단계에서의 침하 예측은 주로 이론적 침하 예측 방법에 의해 수행되지만, 정확도의 문제로 인해 시공 단계에서는 주로 시간에 따른 침하량 계측 결과를 토대로 장래 침하량을 예측하는 계측 기반 침하 예측 방법을 적용하고 있다. 계측 기반 침하 예측 방법 중에서도 쌍곡선법이 주로 쓰이고 있으나 기존의 쌍곡선법은 정확도가 떨어지며 통계적 측면에서 한계점이 명확하기 때문에, 가중 비선형 회귀 분석 기반의 쌍곡선법이 제안된 바 있다. 본 연구에서는 가중 비선형 회귀 쌍곡선법에 두 가지 가중치 부여 방식을 적용하여 침하 예측 정확도를 비교 분석하였다. 부산 신항에 위치한 두 현장에서 측정한 지표침하판 데이터를 활용했으며, 회귀분석 구간을 전체 데이터에 30, 50, 70%로 설정해 나머지 구간의 침하를 예측했다. 그 결과, 가중치 부여 방식과 무관하게 쌍곡선법 기반의 침하 예측 방법은 모두 회귀 분석 구간이 증가할수록 정확도가 높게 나타났으며, 가중 비선형 회귀 쌍곡선법을 통해 기존 선형 회귀 쌍곡선법 보다 정확하게 침하를 예측할 수 있었다. 특히 더 작은 회귀분석 구간이 적용되었음에도 가중 비선형 회귀 쌍곡선법이 기존 선형 회귀 쌍곡선법에 비해 높은 침하 예측 성능을 보여, 가중 비선형 회귀 쌍곡선법을 통해 훨씬 빠르고 정확하게 침하량을 예측할 수 있음을 확인했다.

• Structural Engineering and Mechanics
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• 제35권4호
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• pp.431-457
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• 2010

This article provides a discussion of the mathematic modeling of connections for designing and qualifying structures, systems, and components subject to monotonic or cyclic loading. To characterize the force-deformation behavior of connections under monotonic loading, a review of the Ramberg-Osgood, Richard-Abbott, and Menegotto-Pinto models is conducted, and it is shown that these nonlinear functions can be mathematically derived by scaling up or down a linear force-deformation function. A generalized four-parameter model for simulating connection behavior is investigated to facilitate nonlinear regression analysis. In order to perform seismic analysis of frameworks, a hysteretic model accounting for loading, unloading, and reloading is described using the established monotonic model. For preliminary analysis, a method is provided to quickly determine the model parameters that fit approximately with the observed data. To reach more accurate values of the parameters, the methods of nonlinear regression analysis are investigated and the modified Levenberg-Marquardt and separable nonlinear least-square algorithms are applied in determining the model parameters. Example case studies illustrate the procedure for the computation through the use of experimental/analytical data taken form the literature. Transformation of connection curves from the three-parameter model to the four-parameter model for structural analysis is conducted based on the modeling of connections subject to fire.

• 대한임상검사과학회지
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• 제40권1호
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• pp.18-25
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• 2008

In classical titrimetric analyses, the major concern is the concentration of titrant, usually the aqueous solution of hydrochloric acid or sodium hydroxide, that could be changed as time goes by and it is accompanied with the inaccuracy of the resulting data. And the statistical approach, the nonlinear regression analysis, which is a well-known statistical method, was introduced to determine the accurate concentration of the titrant and the exact value of parameters, $K_a$, r, $C_a$, $C_b$, for 0.01 M aqueous solutions of analytes, sodium pyruvate, sodium acetate, sodium bicarbonate, ammonium hydroxide, ammonium chloride and acetic acid at $25^{\circ}C$. We used Gauss-Newton method for the linearlization of the nonlinear titration system and the two-parameter fitting showed appreciable convergent data for the parameters of the analytes set with the various range of $K_a$ value.

• Archives of Pharmacal Research
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• 제28권5호
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• pp.598-603
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• 2005

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appen dicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected at 48 h after the first dose from all patients at the following times, just before regularly scheduled infusion, at 0.5 h and 2 h after the end of 0.5 h infusion. Serum gentamicin levels were analyzed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12, and 16 ${\mu}g$/mL in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (${\mu}g$/mL) = 0.0246-(0.0495C)+ (0.00203C$^2$). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and better, safer care of patients receiving gentamicin.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.13-15
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• 2000

In this paper, we deal with the asymptotic properties of the regression quantile estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears fer a time series analysis, we study the strong consistency and asymptotic normality of regression quantile ostinators.

• 응용통계연구
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• 제17권1호
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• pp.173-184
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• 2004

Window환경 하에서 사용이 간편하면서도 다양한 통계량을 제공하는 비선형회귀분석을 위 한 통계소프트웨어 NLIN2000을 소개한다. 기존의 DOS용 프로그램을 업그레이드한 것으로 다른 통계 팩키지들에 비하여 모형식의 설정 및 적합과정이 간편하고, 모형식 저장 및 삭제, 모형식 형태 보기 등의 기능을 제공한다. NLIN2000은 비선형회귀분석에 대한 통계적 이론을 연구하는 통계전공자들에게 필수적인 각종 통계량을 제공해줄 뿐만 아니라, 실제 현장에서 비선형모형을 사용하여 분석하는 다른 학문분야의 연구자들에게도 유용하게 사용될 수 있다.

• Journal of the Korean Statistical Society
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• 제29권2호
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• pp.187-199
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• 2000

In this paper, we deal with the asymptotic properties of the least absolute deviation estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears in a time series analysis, we study the strong consistency and asymptotic normality of least absolute deviation estimators. And using the derived limiting distributions we show that the least absolute deviation estimators is more efficient than the least squared estimators when the error distribution of the model has heavy tails.