• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.344-350
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• 1996

An adaptive predictive level control of waste heat steam boiler was studied by using mathematical models considering the inverse response. The simulation experiments of the model identification were performed by using linear and bilinear models. From the results of simulations it was found that the bilinear model represented the actual dynamic behavior of steam boiler very well. ARMA model was used in the model identification and the adaptive predictive controller. To verify the performance and effectiveness of the adaptive predictive controller used in this study the simulation results of the adaptive predictive level control for waste heat steam boiler based on bilinear model were compared to those of P, PI and PID controller. The results of simulations showed that the adaptive predictive controller provides the fast arrival to setpoint of liquid level.

• 자원ㆍ환경경제연구
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• 제19권3호
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• pp.485-508
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• 2010

본 연구에서는 세 개의 대표적 국제원유시장(Brent, Dubai, WTI)의 동적 움직임에 내재되어 있을 수 있는 장기기억 특성과 공적분 관계를 분석한다. 이를 위하여 세 원유시장 일별 가격 시계열자료에 구조변화를 고려한 ARMA-FIAPARCH 결합모형과 벡터오차수정모형(VECM)을 적용하여 추정과 통계적 검정을 수행하였다. 실증분석 결과 세 원유시장 변동성에는 장기기억 특성이 존재하며, ARMA-FIAPARCH 모형이 그러한 장기기억 특성을 잘 포착하는 것으로 나타났다. 또 공적분 검정 결과 공적분 관계가 존재하여 VECM을 추정하였다. VECM 추정 결과에 따르면 원유시장의 수익률과 변동성 사이에는 양방향의 영향이 존재하는 것으로 나타났는데, 이러한 결과는 원유시장의 경우 수익률이 변동성(위험)에 영향을 미치고 또 변동성이 수익률에 영향을 미친다는 것을 의미한다. 이와 같은 발견은 원유시장 구매자 및 판매자들이 원유가격의 동적 움직임을 이해하고 위험을 예측하는데 도움을 줄 것이다.

• 전자공학회논문지B
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• 제30B권5호
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Industrial Engineering and Management Systems
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• 제7권1호
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• pp.44-50
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• 2008

Focusing on the exponential smoothing method equivalent to (1, 1) order ARMA model equation, a new method of estimating smoothing constant using exponential smoothing method is proposed. This study goes beyond the usual method of arbitrarily selecting a smoothing constant. First, an estimation of the ARMA model parameter was made and then, the smoothing constants. The empirical example shows that the theoretical solution satisfies minimum variance of forecasting error. The new method was also applied to the stock market price of electrical machinery industry (6 major companies in Japan) and forecasting was accomplished. Comparing the results of the two methods, the new method appears to be better than the ARIMA model. The result of the new method is apparently good in 4 company data and is nearly the same in 2 company data. The example provided shows that the new method is much simpler to handle than ARIMA model. Therefore, the proposed method would be better in these general cases. The effectiveness of this method should be examined in various cases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.214-217
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• 1987

In this paper self tuning control of interconnected systems are dealt in view point of large scale system control. The plant model is given in multiple ARMA process. This process is simplified as independent SISO ARMA process having offset terms. This offset was considered as effects of interconnections. In each decentralized system, self tuning controller with instrumental variable method is adopted. As a result, this algorithm enables the parameter estimation to be unbiased and non-drift. This controller contains a new implicit offset rejection technique. Simulation results considers well with the analysis in case of linear interconnection.

• 대한전기학회논문지
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• 제37권12호
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• pp.879-883
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• 1988

In this paper, self tuning control of interconnected systems are dealt in view point of large scale system control. The plant model is given in MIMO ARMA procss. This process is simlified as independent SISO ARMA processes having offset terma, which are considered as effects of interconnections. In each decentralized system, self tuning controller with instrumental variable method is adopted. As a result, this algorithm enables the paramter estimation to be unbiased and non-drift. This controller contains a new implicit offset rejection technique. Simulation results consider well with the analysis in case of linear interconnection.

• 동아시아경상학회지
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• 제1권1호
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1991년도 추계학술대회 논문집
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• pp.126-132
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• 1991

Various diagnostic techniques have been proposed according to the development of the engineering. and the machine elements. Specially. since the bearing is an essential component in rotating Machine. Many researchers have been done an earler detection and a exact diagnosis of defects on bearing. In this study, the technique which differ from past diagnosises techniques in frequency domain such as Spectral analysis is proposed. Parametric ARMA model was utilized to define the system using tire series directly. Based on the resulted ARMA parameters, more precies and faster diagnosis system through Neural network was presented.

• Journal of Communications and Networks
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• 제5권1호
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• pp.65-72
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• 2003

Internet roundtrip delay/time (RTT) prediction plays an important role in detecting packet losses in reliable transport protocols for traditional web applications and determining proper transmission rates in many rate-based TCP-friendly protocols for Internet-based real-time applications. The widely adopted autoregressive and moving average (ARMA) model with fixed-parameters is shown to be insufficient for all scenarios due to its intrinsic limitation that it filters out all high-frequency components of RTT dynamics. In this paper, we introduce a novel parameter-varying RTT model for Internet roundtrip time prediction based on the information theory and the maximum entropy principle (MEP). Since the coefficients of the proposed RTT model are updated dynamically, the model is adaptive and it tracks RTT dynamics rapidly. The results of our experiments show that the MEP algorithm works better than the ARMA method in both RTT prediction and RTO estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.147-153
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• 1990

This paper treats modeling of dynamic load characteristics for power for systems. The dynamic load is represented as 4th order multivariable ARMA model under the assumption that the dynamic load characteristics can be described by the dynamics of only one induction motor. The parameters of the proposed ARMA model are identified using the well-known RLS method. This paper presents two kind of identification results : one is for induction motors and the other is for field data at Donghae station. From these results, the proposed model is quite suitable for the dynamic load characteristics. It has, however, a disadvantage in the viewpoint that the identified parameters are not those of the induction motor.