• 제목/요약/키워드: Approximate function

검색결과 655건 처리시간 0.032초

Estimating Variance Function with Kernel Machine

  • Kim, Jong-Tae;Hwang, Chang-Ha;Park, Hye-Jung;Shim, Joo-Yong
    • Communications for Statistical Applications and Methods
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    • 제16권2호
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    • pp.383-388
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    • 2009
  • In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

Estimations of the skew parameter in a skewed double power function distribution

  • Kang, Jun-Ho;Lee, Chang-Soo
    • Journal of the Korean Data and Information Science Society
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    • 제24권4호
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    • pp.901-909
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    • 2013
  • A skewed double power function distribution is defined by a double power function distribution. We shall evaluate the coefficient of the skewness of a skewed double power function distribution. We shall obtain an approximate maximum likelihood estimator (MLE) and a moment estimator (MME) of the skew parameter in the skewed double power function distribution, and compare simulated mean squared errors for those estimators. And we shall compare simulated MSEs of two proposed reliability estimators in two independent skewed double power function distributions with different skew parameters.

비선형 함수 근사화를 사용한 TD학습에 관한 연구 (A study of Temperal Difference Learning using Nonlinear Function Approximation)

  • 권재철;이영석;김독옥;서보혁
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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    • pp.407-409
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    • 1998
  • This paper deals with temporal-difference learning that is a method for approximating long-term future cost as a function of current state in knowlege-poor environment, a function approximator is used to approximate the mapping from state to future cost, a linear function approximator is limited because mapping from state to future cost has a nonlinear characteristic, so a nonlinear function approximator is used to approximate the mapping from state to future cost in this paper, and that TD learning using a nonlinear function approximator is stable is proved.

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Support Vector Quantile Regression with Weighted Quadratic Loss Function

  • Shim, Joo-Yong;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • 제17권2호
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    • pp.183-191
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    • 2010
  • Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.

New Empirical Approach to Enhance The Accuracy of Cannon Tube Erosion Rate

  • Chung, Dong-Yoon;Oh, Myoung-Ho
    • 한국윤활학회:학술대회논문집
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    • 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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    • pp.231-232
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    • 2002
  • Various methods that utilize erosion rate measurement of standard cannon, 155mm Howitzer M185, as reference, are being used to calculate erosion rate of an interested unknown cannon tubes. We know ten measured erosion values of the standard cannon from 391 rounds to 4.000. An approximate function fitting these value s is derived. The new erosion equation is also suggested and computer simulations arc presented.

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THE CAPABILITY OF LOCALIZED NEURAL NETWORK APPROXIMATION

  • Hahm, Nahmwoo;Hong, Bum Il
    • 호남수학학술지
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    • 제35권4호
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    • pp.729-738
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    • 2013
  • In this paper, we investigate a localized approximation of a continuously differentiable function by neural networks. To do this, we first approximate a continuously differentiable function by B-spline functions and then approximate B-spline functions by neural networks. Our proofs are constructive and we give numerical results to support our theory.

Censored Kernel Ridge Regression

  • Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1045-1052
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    • 2005
  • This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The weighted data are formed by redistributing the weights of the censored data to the uncensored data. Then kernel ridge regression can be taken up with the weighted data. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized approximate cross validation(GACV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

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퍼지 제어기의 Scaling Factor의 영향 분석 (Analysis of the Effects of Scaling Factors of Fuzzy Controller)

  • 이철희;서선학
    • 산업기술연구
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    • 제15권
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    • pp.195-202
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    • 1995
  • In this paper, we analyze the effects of scaling factors on the performance of a fuzzy controller. The quantitative relation between input and output variables of a fuzzy controller is obtained by using a quasi-linear fuzzy model. And an approximate transfer function of a fuzzy controller is derived from the comparison of fuzzy controller with the conventional PID controller. Then we analyze the effects of scaling factor using this approximate transfer function and root locus method.

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Estimation for the extreme value distribution under progressive Type-I interval censoring

  • Nam, Sol-Ji;Kang, Suk-Bok
    • Journal of the Korean Data and Information Science Society
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    • 제25권3호
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    • pp.643-653
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    • 2014
  • In this paper, we propose some estimators for the extreme value distribution based on the interval method and mid-point approximation method from the progressive Type-I interval censored sample. Because log-likelihood function is a non-linear function, we use a Taylor series expansion to derive approximate likelihood equations. We compare the proposed estimators in terms of the mean squared error by using the Monte Carlo simulation.

보수적 근사모델을 적용한 신뢰성 기반 강건 최적설계 방법 (Study of Reliability-Based Robust Design Optimization Using Conservative Approximate Meta-Models)

  • 심형민;송창용;이종수;최하영
    • 한국해양공학회지
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    • 제26권6호
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    • pp.80-85
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    • 2012
  • The methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) were implemented in the present study. RBRDO is an integrated method that accounts for the design robustness of an objective function and for the reliability of constraints. The objective function in RBRDO is expressed in terms of the mean and standard deviation of an original objective function. Thus, a multi-objective formulation is employed. The regressive approximate models are generated via the moving least squares method (MLSM) and constraint-feasible moving least squares method (CF-MLSM), which make it possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.