• 제목/요약/키워드: ESTIMATOR model

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지능형 속도 추정기를 이용한 유도전동기의 센서리스 속도제어 (Sensorless Speed Control of Induction motor using the Intelligent Speed Estimator)

  • 박진수;최성대;김상훈;윤광호;반기종;남문현;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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    • pp.660-662
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    • 2004
  • This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

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A Comparative Study on Nonparametric Reliability Estimation for Koziol-Green Model with Random Censorship

  • Cha, Young-Joon;Lee, Jae-Man
    • Journal of the Korean Data and Information Science Society
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    • 제8권2호
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    • pp.231-237
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    • 1997
  • The Koziol-Green(KG) model has become an important topic in industrial life testing. In this paper we suggest MLE of the reliability function for the Weibull distribution under the KG model. Futhermore, we compare Kaplan-Meier estimator, Nelson estimator, Cheng & Chang estimator, and Ebrahimi estimator with proposed estimator for the reliability function under the KG model.

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Estimation of the Polynomial Errors-in-variables Model with Decreasing Error Variances

  • Moon, Myung-Sang;R. F. Gunst
    • Journal of the Korean Statistical Society
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    • 제23권1호
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    • pp.115-134
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    • 1994
  • Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

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유도전동기의 속도 센서리스 제어를 위한 지능형 속도 추정기의 설계 (Design of Intelligent Speed Estimator for Speed Sensorless Control of Induction Motor)

  • 박진수;최성대;김상훈;고봉운;남문현;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2004년도 하계학술대회 논문집 D
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    • pp.2304-2306
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    • 2004
  • This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

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차량 전복 방지를 위한 강건한 롤 상태 추정기 설계 (Design of a Robust Estimator for Vehicle Roll State for Prevention of Vehicle Rollover)

  • 박지인;이경수
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2007년도 춘계학술대회A
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    • pp.1103-1108
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    • 2007
  • This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

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An On-line Rotor Resistance Estimator for Induction Machine Drives

  • Kwon, Chun-Ki
    • Journal of Power Electronics
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    • 제9권3호
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    • pp.354-364
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    • 2009
  • Rotor resistance variation due to changing rotor temperature is a significant issue in the design of induction motor controls. In this work, a new on-line rotor resistance estimator is proposed based on an alternate qd induction machine model which provides better mathematical representation of an induction machine than the classical qd model (which uses constant parameters). This is because the former simultaneously includes leakage saturation, magnetizing path saturation, and distributed circuit effects in the rotor conductors. The comparisons via computer simulation studies show the ability of the proposed estimator to accurately track rotor resistance variation. For the experimental studies, due to the difficulty in measuring the actual rotor resistance, comparison of the controller performance using the proposed estimator, the classical qd model based estimator, and no estimator is made.

Partially Parametric Estimation of Lifetime Distribution from a Record of Failures and Follow-Ups

  • Yoon, Byoung Chang
    • 품질경영학회지
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    • 제22권4호
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    • pp.59-78
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    • 1994
  • In some observational studies, we have often random censoring model. However, the data available may be partially observable censored data consisting of the observed failure times and only those nonfailure times which are subject to follow up. In this paper, we present an extension of the problem of partially parametric estimation of the survival function to such partially observable censored data. The proposed estimator treats the observed failure times nonparametrically and uses a parametric model only for those nonfailure times which are subject to follow-up. We discuss the motivation and construction of the proposed estimator and investigate the limiting properties of the proposed estimator such as asymptotic normality. Also, when the assumed parametric model is exponential, the asymptotic variance of the estimator is obtained. Furthermore, an example is given to compare the proposed estimator with the modified Kaplan Meier(MKM) estimator. From the results, it is shown that the relative efficiency of the proposed estimator is higher than that of the MKM estimator in the follow-up study with increasing time.

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수확예측(收穫豫測) Model의 Multicollinearity 문제점(問題點) 해결(解決)을 위(爲)한 Ridge Regression의 이용(利用) (The Use Ridge Regression for Yield Prediction Models with Multicollinearity Problems)

  • 신만용
    • 한국산림과학회지
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    • 제79권3호
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    • pp.260-268
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    • 1990
  • 수확(收穫) 예측(豫測) model이 multicollinearity 문제점(問題點) 가질때 보다 정확한 추정식(推定式)을 얻기 위하여 두 종류의 ridge estimator와 최소(最小) 자승법(自乘法)(OLS)의 추정치를 비교(比較)하였다. 본 연구(硏究)에서 사용(使用)된 ridge estmator는 Mallows's (1973)Cp-like statistic과 Allens's (1974) PRESS-like statistic 이었다. 위의 세가지 estimator 예측(豫測) 능력(能力) 평가(評賣)는 Matney 등(等)(1988)에 의하여 개발(開發)된 수확(收穫) model을 이용(利用)하여 비교(比較)하였다. 사용되어진 자료(資料)는 미국(美國) 남부(南部) 테에다 소나무 시험림(試驗林)의 총(總)522개(個) plot을 이용(利用)하였다. 두 개(個)의 ridge estimator가 최소(最小) 자승법(自乘法)에 의한 추정치 보다 수확(收穫) 예측(豫測) 능력(能力)이 우수(優秀)하였으며, 특히 Mallows's statistic에 의한 ridge estimator가 가장 우수(優秀)하였다. 따라서 ridge estimator는 수확(收穫) 예측(豫測) model의 독립(獨立) 변수(變數) 간(間)에 multicollinearity 문제점(問題點)이 있을 때 최소(最小) 자승법(自乘法)에 의 한 추정치를 대치(代置)할 수 있는 estimator로서 추천(推薦)할 수 있었다.

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Nonparametric Estimation in Regression Model

  • Han, Sang Moon
    • Communications for Statistical Applications and Methods
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    • 제8권1호
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    • pp.15-27
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    • 2001
  • One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

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Generalized Bayes estimation for a SAR model with linear restrictions binding the coefficients

  • Chaturvedi, Anoop;Mishra, Sandeep
    • Communications for Statistical Applications and Methods
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    • 제28권4호
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    • pp.315-327
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    • 2021
  • The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.