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.
Highlights
The model of the system is often used in research to perform experiments instead of a real system, to optimize system performances and to design system control
From the results shown in “Figure 5”, it is obvious that artificial neural networks (ANNs) is able to identify the system even in case when there are interferences present in the output signal, while least squares estimate (LSE) obtained model has no such ability
Neural model follows the response of the actual system, while LSE method model was not able to track the response of the actual system
Summary
The model of the system is often used in research to perform experiments instead of a real system, to optimize system performances and to design system control. The possibility of forming a reliable system model is of great importance. The model can be formed on the basis of the measured values of input and output variables of the system. That model is called the experimental model, and the process of forming the model is called identification, i.e. parameter identification. The experimental model, it does not give insight to the physical properties of the system, is easier to form and better describes the input-output behaviour of the system. This property makes it suitable for control system designing, as well as prediction of the system behaviour.
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