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System identification with particle swarm optimization method for nonlinear dynamic systems
Conference paper

System identification with particle swarm optimization method for nonlinear dynamic systems

Manuel A. Fernández and Jen-Yuan James Chang
ASME 2020 29th Conference on Information Storage and Processing Systems, ISPS 2020, V001T01A001
2020

Abstract

Noisy data Particle swarm optimization PSO System identification Control and Systems Engineering Information Systems Hardware and Architecture
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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