• 조명전기설비학회논문지
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• 제18권4호
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• pp.83-87
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• 2004

본 논문에서는 Magnetic Levitation Model 시스템을 이용하여 선형 액추에이터의 기본모델을 제안하고 전원 공급부의 입력 주파수의 변화로부터 액추에이터에 나타나는 현상을 Matlab프로그램을 활용하여 간접적 방법으로 고찰하였다. 그 결과는 실제적인 설계에 적용할 경우 설계 프로그램의 자료나 부분적 변경 시 참조 할 수 있으리라 사료된다. 본 논문에서 제안한 에너지 변환을 고려한Linear Actuator M3d진 시스템의 출력은 입력 주파수의 변화로부터 액추에이터가 고주파보다는 저주파에서 권선부에 나타나는 파형의 응답이 기준 입력파형에 더 근접함을 알 수 있었다. 이 결과를 바탕으로 Linear Actuator Model 시스템의 동작 시 특성이 실제 시스템에 활용할 때 간접적 방법으로 상당히 유용함을 확인 할 수 있었다.

• 한국시스템다이내믹스연구
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• 제8권2호
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• pp.253-273
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• 2007

To measure the effect of school zone on housing cost, Linear Regression Model is widely used, and school zone is known as a key determinant of housing cost in Korea. However, when the Hierarchical Linear Model (HLM) is applied with the same data, school effect on housing cost becomes statistically non-significant. It is because HLM effectively separates the effect of individual housing's attributes from the group effect. In sum, the housing cost of Kangnam, where good public schools are located, is apparently is higher than that of Kangbuk. However, the school effect on housing cost (Level 2) becomes non-significant when individual housing's attributes (Level 1) are controlled with HLM.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• 전자공학회논문지SC
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• 제41권2호
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• pp.29-36
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• 2004

본 논문에서는 시변 시간지연을 갖는 선형시스템의 분수 모델 간략화를 다룬다. 이를 위해 선형 시간지연 시스템의 축소된 소인수 분해를 정의하고 선형 행렬부등식의 해를 이용하여 구한다. 축소된 소인수의 일반화 가제어성, 가관측성 그래미안을 이용하여 시스템의 균형화된 상태공간 모델을 구현한다. 모델 차수축소는 균형화된 상태공간 모델의 일부 상태변수를 절삭하여 얻어지며 모델 오차의 상한치를 제시한다. 제안된 방법의 효용성을 수치 예를 통하여 입증한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1992년도 제2회 정기총회 및 추계학술 발표회 발표논문 초록
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• pp.8-8
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• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.

• 한국컴퓨터산업학회논문지
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• 제7권3호
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• pp.253-262
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• 2006

폐쇄된 계 내에서 발생하는 운동과 계 외에서 작용하는 힘에 의해 발생하는 운동은 뚜렷한 차이가 있다. 계 내에서 발생하는 운동에 의해 외부로 운동이 나타난 경우 닫힌 운동이고 계 외에서 원인으로 한 방향으로 발생하는 운동은 열린 운동이다. 닫힌 운동 모델은 선형 닫힌 운동계와 비선형 닫힌 운동계가 있다. 선형 닫힌 운동의 원리와 종류 및 실험 장치를 통하여 근사 수식모델을 만들고 여러 가지 비선형 닫힌 운동 모델 종류와 실험 장치를 비교하였다. 또한 비선형 닫힌 운동 모델이 조합되어 선형 닫힌 운동 모델이 될 수 있음을 알 수 있다.