Applying Modified Discrete Particle Swarm Optimization Algorithm and Genetic Algorithm for system identification

A system identification problem can be formulated as an optimization task where the objectives are to find a model and a set of parameters that minimize the prediction error between the plant output and the model output. This paper presents a technique for identifying the parameters of system using...

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Hauptverfasser:	Badamchizadeh, M A, Madani, K
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Buildings Discrete particle swarm optimization algorithm Evolution (biology) Evolutionary computation Genetic algorithm Genetic algorithms Noise robustness Particle swarm optimization Power system modeling Predictive models Process control System identification
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creator	Badamchizadeh, M A Madani, K
description	A system identification problem can be formulated as an optimization task where the objectives are to find a model and a set of parameters that minimize the prediction error between the plant output and the model output. This paper presents a technique for identifying the parameters of system using Genetic Algorithms and the Modified Discrete Particle Swarm Optimization Algorithm. Derived from a step test a robust identification method for process is proposed. The simulation results show suggested methods are robust in the presence of large amounts of measurement noise, and discrete particle swarm optimization algorithm has a lower cost value than Genetic Algorithm.
doi_str_mv	10.1109/ICCAE.2010.5451412
format	Conference Proceeding
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subjects	Buildings Discrete particle swarm optimization algorithm Evolution (biology) Evolutionary computation Genetic algorithm Genetic algorithms Noise robustness Particle swarm optimization Power system modeling Predictive models Process control System identification
title	Applying Modified Discrete Particle Swarm Optimization Algorithm and Genetic Algorithm for system identification
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