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.

Volume Subject Area:
Advanced Simulation in Science and Engineering
Keywords:
system identification, particle swarm optimization, PSO, noisy data
Topics:
Nonlinear dynamical systems, Particle swarm optimization
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