• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.3
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• pp.237-246
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• 1989

A sliding memory covariance circular lattice (SMC-CL) filter and an efficient ARMA modeling method using the SMC-CL filter are presented. At first, SMC-CL filter is derived based on the geometric approach. Then ARMA process is converted into 2 channel AR process, and SMC-CL filter is applied to it. The structure of SMC-CL filter becomes simpler in case of ARMA modeling due to the whiteness of a driving input process. The parameters of ARMR process can be obtained by the Levinson recursions from the PARCOR coefficients of the second channel of the filter. Computer simulations are performed to show the effctiveness of the proposed algorithm.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.339-345
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• 2013

In this paper, we give a tractable sufficient condition for functional central limit theorem to hold in Markov switching ARMA (p, q) model.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.380-385
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• 1990

This Paper presents a general algorithm for the parameter estimation of an antoregressive moving average process observed in additive white noise. The algorithm is based on the Gauss-Newton recursive prediction error method. For the parameter estimation, the output measurement is modelled as an innovation process using the spectral factorization, so that noise free RPE ARMA estimation can be used. Using apriori known properties leads to algorithm with smaller computation and better accuracy be the parsimony principle. Computer simulation examples show the effectiveness of the proposed algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• Communications of the Korean Mathematical Society
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• v.21 no.3
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• pp.533-541
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• 2006

We consider a autoregressive moving average process of order p and q with Markov switching coefficients and find sufficient conditions for irreducibility of the process. Identifying small sets is also examined.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.181-187
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• 1992

본 논문은 CIMS에서 적응되는 ARMA 공정제어의 새로운 3단계절차를 제안한다. 첫번째 단계는 다변량 ARMA모델을 식별하여 모수를 추정하고, white noise로 진단된 잔차 series에 대하여 다변량 제어통계량(즉, 다변량 Hotelling T$^2$통계량, 다변량 CUSUM, 다변량 EWHA 통계량, 다변량 MA 통계량)등을 계산한다. 마지막으로 본 논문에서 제안한 8가지 다변량 제어통계량을 상호비교하여 이상점을 발견한다.

• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.135-135
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• 1997

Times series modeling plays an important role in the field of engineering, Statistics, Biomedicine etc. Model identification is one of crucial steps in the modeling of an AutoRegreesive Moving Average(ARMA(p, q)) process for real world problems. Many techniques have been developed in the literature (Salas et al., McLeod et al. etc.) for the identification of an ARMA(p, q) Model. In this paper, a new technique called The Generalised Parameters Technique is formulated for seasonal and non-seasonal ARMA model identification. This technique is very simple and can e applied to any given time series. Initial estimates of the AR parameters of the ARMA model are also obtained by this method. This model identification technique is validated through many theoretical and simulated examples.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.262-264
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• 2005

We propose a filter design method to suppress the effect of gyroscope mixed random errors at INS system level. It is based on the result that mixed random errors can be represented by a single equivalent ARMA model. At first step, the time difference of equivalent ARMA process is performed, which consider the characteristic of indirect feedback Kalman filter used in INS filter. Next, a state space conversion of time differenced ARMA model is achieved. If the order of AR is greater than that of MA, the controllable or observable canonical form is used. Otherwise, we introduce the state equation of which the state variable is composed of the ARMA model output and several step ahead predicts of that. At final step, a complete form state equation is presented. The simulation results shows that the proposed method gives less transient error and better convergence compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.301-314
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• 2002

For autoregressive moving average (ARMA) models, robust unit root tests are developed using M-estimators. The tests are parametric in the sense ARMA parameters are estimated jointly with unit roots. A Monte-Carlo experiment reveals superiority of the parametric tests over the semipararmetric tests of Lucas (1995a) in terms of both empirical sizes and powers.