• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.83-85
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• 1986

The error differential equations which are derived by using the first error model are uniformly asymptotial stable if the input is bounded and sufficiently rich. In the adaptive control, the speed of convergence of system output or parameter error in such cases is of both practical and theoretical interest. In this paper, the adaptive algorithms(Gradient algorithm, Intergral algorithm) are discussed from the point of view of speed convergence and the modification of adaptive law for prohibition of overadaptation is discussed. The result is compared among this algorithms and the adaptive gain is choosed by this result(the speed of convergence).

• 대한수학회보
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• 제44권1호
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• 한국안전학회지
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• 제36권3호
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• pp.60-65
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• 2021

Human error is often in part in the cause of accidents and the result of various factors in an organization. Accidents should be investigated to elucidate all causes. Therefore, to reduce accidents, it is necessary to identify which factors affect human error within the organization. In this study, five groups of influencing factors on human error were selected using previousresearch, and operational definitions were made based on them. In addition, a questionnaire for measuring latent variables by operational definition was developed as an observation variable, and responses were received from employees of chemical companies in Ulsan. Based on SEM (structural equation modeling) analysis, 1) confirmatory factor analysis of variables in the human error model, 2) reliability and validity of latent variables, 3) correlations among latent variables, 4) influencing coefficients among influence factors, and 5) the verification results of the paths that these influencing factors have on human error are introduced in this study.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.176.4-176
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• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.

• 한국소음진동공학회논문집
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• 제12권8호
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• pp.616-625
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• 2002

In this paper, we drove the filtered-x least mean fourth (FXLMF) algorithm where the error raised to the power of four is minimized and analyzed its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. The application of the FXLMF adaptive filter to active noise control requires to estimate the transfer characteristics of the acoustic path between the output and the error signal of the adaptive controller. The results of the convergence analysis of the FXLMF algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that the convergence behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant.

• 대한전자공학회논문지
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• 제26권1호
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• pp.1-9
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• 1989

적응제어의 수렴분석 방법이 지난 수십년간 연구되어 왔으나, 적응제어 시스템의 고속수렴에 대한 구조적 중요성은 아직도 논쟁이 되고 있다. 본 논문은 적응제어 error 모델에 대한 지수적 수렴속도의 상대적 개선방법을 고찰하였다. Error시스템의 궤환구조를 변화시킴으로써 수렴속도를 개선하기 위해Lyapunov의 직접방식을 적응제어 시스템에 적용하였다. 몇가지 simulation 예를 들어, 이 방법의 고속수렴과 robustness를 보였다.

• The Journal of the Acoustical Society of Korea
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• 제15권3E호
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• pp.66-73
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• 1996

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise control(ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• 한국통신학회논문지
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• 제26권12A호
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• pp.2050-2058
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• 2001

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of 7he convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1998년도 학술발표대회 논문집 제5권
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• pp.80-83
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• 1998

In this paper, we propose a new filtered-x least mean fouth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior or a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the ouput and error signal of the adaptive canceller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct component . Phase estimation error and estimated again. In particular , the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• 전자공학회논문지B
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• 제32B권11호
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• pp.1506-1516
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• 1995

In this paper, we propose the filtered-x least mean fourth (FXLMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the FXLMF adaptive filter to active noise control requires to estimate the transfer characteristics of the acoustic path between the output and the error signal of the adaptive controller. The results of the convergence analysis of the FXLMF algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that the convergence behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant.