MIMO Frequency Sampling Filters for MIMO Frequency Sampling Filters for Modelling of MIMO System Applications*  

Oleh:

Mohd-Mokhtar, Rosmiwati ; Aziz, Muhammad Hilmi R.A.

Jenis: Article from Journal - ilmiah nasional - terakreditasi DIKTI
Dalam koleksi: Journal of Engineering and Technological Sciences vol. 45 no. 01 (2014), page 073-096.
Topik: Data compression parametric model; system identification. compression; frequency sampling filters; multivariable process system identification. multivariable process; non-
Fulltext: 623-1905-1-SM.pdf (272.45KB)

Isi artikel

In the modelling of a system based on approach, data acquisition is the first procedure that must be data acquisition process from data. This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance high frequency region. In addition, bias estimatio considers direct identification from a closed problem, in this paper a introduced. Multi-input obtain only relevant model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling filters was demonstrated by process data and steam generator plant data. raw data was successfully compressed and identification purposes with less computational time used to develop a model of the system, analysis, and also for developing a new modelling of a system based on a system identification approach, data acquisition is the first procedure that must be carried out. acquisition process from a real system typically yields large amounts of This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance, especially in high frequency region. In addition, bias estimation will occur if one only considers direct identification from a closed-loop system. To overcome in this paper a multivariable frequency sampling filter approach is input-multi-output (MIMO) raw data are analyzed in order to and meaningful parameters that describe the empirical model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling demonstrated by compressing two sets of data: pH neutralization process data and steam generator plant data. The results show that the amount of successfully compressed and that the output was ready for identification purposes with less computational time, i.e. they could be further used to develop a model of the system, to conduct time and frequency response developing a new control system design. system identification carried out. The real system typically yields large amounts of This may lead to unacceptable computational time during the identification in the n will occur if one only loop system. To overcome this multivariable frequency sampling filter approach is output (MIMO) raw data are analyzed in order to that describe the empirical model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling compressing two sets of data: pH neutralization that the amount of ready for be further conduct time and frequency response nalysis, and also for developing a new control system design.

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