Original scientific paper

https://doi.org/10.7225/toms.v04.n02.001

System Identification in Difficult Operating Conditions Using Artificial Neural Networks

Petar Matić orcid.org/0000-0002-1799-5257 ; University of Split, Faculty of Maritime Studies in Split, Split, Croatia
Ivana Golub Medvešek ; University of Split, Faculty of Maritime Studies in Split, Split, Croatia
Tina Perić orcid.org/0000-0002-9531-0065 ; University of Split, Faculty of Maritime Studies in Split, Split, Croatia

Abstract

To investigate an ability of system identification in difficult operating conditions. A simulation based experiment was performed on a simple second order system with white noise signal superimposed to the output signal. Interferences are added to the output signal in order to simulate difficult operating conditions present in a real system environment. Based on system simulation measurements, the system was identified using conventional method with least squares estimate and an alternative method, a multi-layer perceptron (MLP) network. Graphical evaluation of simulation results showed that MLP network produced better results than conventional model, with significantly better results in case of interferences in the output signal. To model dynamic system, a simple two-layer perceptron network with external dynamic members was trained in Matlab using Levenberg-Marquardt algorithm.

Keywords

System identification; Difficult operating conditions; Artificial neural network; Multy-layer perceptron; Levenberg-Marquardt

Hrčak ID:

147237

URI

https://hrcak.srce.hr/147237

Publication date:

21.10.2015.

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