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Type: Artigo de evento
Title: Mechanical System Identification By Multivariate Time Series Models
Author: Baldeon Amaro R.P.
Kurka P.R.G.
Abstract: The work presents a Multivariate Time Series Models in the identification of mechanical systems. The maximum likelihood technique (ML) is applied to estimate the parameters of the time series models in order to improve the precision in the estimation of modal parameters. Spliid's algorithm is used to estimate initial values to start the iterative process of the ML technique. The performance of the ML technique is verified in a three degree freedom simulated system with two inputs and two outputs. Stochastic noise is added to the outputs in order to verify the performance of the time series model in the presence of stochastic noise. © (2006) by the Katholieke Universiteit Leuven Department of Mechanical Engineering All rights reserved.
Editor: Katholieke Universiteit Leuven
Rights: fechado
Identifier DOI: 
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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