Nonlinear damping identification in nonlinear dynamic system based on stochastic inverse approach
type: Item
instance of: scholarly article
title: Nonlinear damping identification in nonlinear dynamic system based on stochastic inverse approach
zbMATH Open document ID: 1264.93254
DOI: 10.1155/2012/574291
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: The nonlinear model is crucial to prepare, supervise, and analyze mechanical system. In this paper, a new nonparametric and output-only identification procedure for nonlinear damping is studied. By introducing the concept of the stochastic state space, we formulate a stochastic inverse problem for a nonlinear damping. The solution of the stochastic inverse problem is designed as probabilistic expression via the hierarchical Bayesian formulation by considering various uncertainties such as the information insufficiency in parameter of interests or errors in measurement. The probability space is estimated using Markov chain Monte Carlo (MCMC). The applicability of the proposed method is demonstrated through numerical experiment and particular application to a realistic problem related to ship roll motion.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6173392
Wikidata QID: Q58912157
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/574291
OpenAlex ID: W2020357499
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description: scientific article; zbMATH DE number 6173392