Identification of Stochastic Process in MATLAB

Ojonugwa Adukwu


The system identification toolbox in MATLAB has been successfully used to compare model identification of a first order system subjected to high and low disturbances. The model structures used are FIR, ARX, AMX, OE and BJ. The obtained Model was validated using data generated from the actual process. It shows that the more the variance of the noise input into the system, the more difficult it is for the model identified to reproduce that validation data obtained from process response. Also when the measurement noise has zero mean and low variance, the effect on the steady state gain and other process parameters is negligible.


System Identification, model structures, process noise

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Kollar I., Pintelon R., Schoulkens J. (1991), Frequency Domain Identification ToolBox for MATLAB, IFAC Proceedings, 24(3), pp 1243-1247.

Jimenez M.J., Madsen H., Anderson K.K., (2008), Identification of the Main Characteristics of Building Components Using MATLAB, Journal of Building and Environment, 43(2), pp. 170-180.

Yuen K.V., Mu H.Q. (2015)., Real-Time Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification, Journal of Computer-Aided Civil and Infrastructure Engineering, pp 1-17.

Adukwu O., Oku D. E. (2018), Modeling Analysis and Monitoring DC Motor Using Proportional-Integral Controller and Kalman Filter, International Journal of Science and Research, 7(8), pp 454-458.

Ljung L. (1987), System Identification: Theory for User, Prentice-Hall, Englewood Cliffs, NJ, USA.

Ljung L. (1998), System Identification, Birkhäuser, Boston, MA, USA.



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