• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.135-142
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• 2009

In this paper the Simplified Refined Instrumental Variable (SRIV) identification algorithm for SISO systems is extended to MIMO systems described by a Left Matrix Fraction Description (LMFD). The performance of the extended algorithm is compared to the well-known MIMO four-step instrumental variable (IV4) algorithm. Monte Carlo simulations for different signal to noise ratios are conducted to assess the performance of the algorithm. Moreover, the algorithm is applied to a simulated quadruple tank process.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.4
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• pp.156-162
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• 1986

The last few years have seen a rapid development of so-called lattice algorithms for the fast solution of finite date algorithms. So far, most of the work on ladder form has been done for the prewindowed case. In this paper, the covariance lattice algorithm for instrumental variable recusions is presented. This algorithm can be used in various areas of adaptive signal processing, spectral estimation and system identification. The behavior of the proposed algorithm is illustrated by some simulation results for spectral estimation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.10
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• pp.438-444
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• 1986

The signal can be modeled as a linear combination of its past values and present and past values of a hypothetical input to system whose output is given signal. Using this model spectral estimation problem can be reduced to estimate the ARMA parameters. This paper presents recursive modified instrumental variable algorithm which can estimate AR and MA parameters. For more accurate estimation, overdetermined modified IV algorithm is also derived. Computer simulations are presented to illustrate the above methods.

• The Journal of the Acoustical Society of Korea
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• v.16 no.3
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• pp.81-85
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• 1997

In this paper, a recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. To obtain the Overdetermined Recursive Instrumental Variable(ORIV) method type algorithm, we transform the 3'th and 4'th order cumulant relationship to a certain matrix form which is consist of only output data. From the matrix form, we induce the proposed algorithm procedure following the ORIV method. The proposed algorithm provides improved estimation accuracy with smaller data and can be applied to a time varying system as well. In addition, it reduces the estimation error due to the additive Gaussian noise compared to conventional 2'rd order based algorithms since it only uses higher than 2'rd order cumulant. Simulation results are presented to compare the performance with other HOS-based algorithms.

• Journal of KSNVE
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• v.6 no.5
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• pp.679-684
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• 1996

The Instrumental Variable Method(IVM), modified from least square algorithm, is applied to parameter identification of a noise contaminated bearing test rig. The signal to noise ratio included in Frequency Response Function(FRF) can cause significant errors in parameter identification. Therefore, among several candidates of parameter identification method, results of the applied IVM were compared with noise-contaminated least square method. This study shows that the noise-contaminated least square method can have indonsistent accuracy depending on the degree of noise level, while the IVM has robuster performance to signal to noise ratio than least square method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.3
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• pp.354-362
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• 2000

This paper identifies a discrete-time linear combustion model of Refuse Incineration Plant(RIP) which characterizes steam generation quantity, where the RIP is considered as a MIMO system with thirteen-inputs and one-output. The structure of RIP model is described as an ARX model which are analytically obtained from the combustion dynamics. Furthermore, using the Instrumental Variable(IV) identification algorithm, model structure and unknown parameters are identified from experimental input-output data sets, In result, it is shown that the identified ARX model well approximates the input-output combustion characteristics given by experimental data sets.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.312-320
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• 1990

Analysis and lattice implementation of Extended Instrumental Variable (EIV) methods for high resolution spectral analysis are presented. The performance of EIV is improved by using prefilters and the unbiasness of EIV is proved by using the fact that residual processes are white. We derive the order and time update formulas for the covariance lattice algorithm which is particularly useful in case of short data or nonstationary processes. The ARMA model can be modeled as two channel AR processes. Using this model, the lattice algorithms of EIV are derived. Computer simulations are performed to show the usefulness of the proposed algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.63-68
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• 2017

This paper reviews the learning model of a forward slip ratio in order to improve the mass-flow stability and strip qualities in the hot strip finishing mill process. Firstly, it is shown, from mathematical analysis, that the significant parameters of the forward slip ratio are the tension, looper angle, and roll velocity. Secondly, a discrete-time learning model of the forward slip ratio is proposed from these parameters, which is identified by an instrumental variable (IV) identification algorithm. Finally, it is shown from computer simulation that the proposed learning model is more effective than the existing learning model.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.556-562
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• 2002

This paper studies the identification of a combustion model, which is used to design a linear controller of a steam generation quantity and harmful exhaust gases of a Refuse Incineration Plant(RIP). Even though the RIP has strong nonlinearities and complexities, it is identified as a MIMO parametric ARX model from experimental input-output data sets. Unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. It is shown that the identified model well approximates the input-output combustion characteristics.