System Identification With Particle Swarm Optimization Method for Nonlinear Dynamic Systems
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Abstract This paper presents a comparison between different system identification techniques, namely Least Squared Estimation, Total Least Squares, Linear Sequential Estimation, the Gauss-Newton method, and Particle Swarm Optimization. A DC motor model was simulated in Simulink, with arbitrarily selected parameters, and the input and output values were used to test the effectiveness of these system identification techniques.
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