Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/73969
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorChen, Z-
dc.creatorNi, Y-
dc.date.accessioned2018-03-29T07:15:46Z-
dc.date.available2018-03-29T07:15:46Z-
dc.identifier.issn1000-3835-
dc.identifier.urihttp://hdl.handle.net/10397/73969-
dc.language.isozhen_US
dc.publisher中國學術期刊 (光盤版) 電子雜誌社en_US
dc.rights© 2017 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.en_US
dc.rights© 2017 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。en_US
dc.subjectBayesian regularizationen_US
dc.subjectGeneralizationen_US
dc.subjectMagnetorheological damperen_US
dc.subjectNARX networken_US
dc.subjectNonparametric modelen_US
dc.titleEnhanced generalization of nonparametric model for magnetorheological dampersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage146-
dc.identifier.epage151 and 167-
dc.identifier.volume36-
dc.identifier.issue6-
dc.identifier.doi10.13465/j.cnki.jvs.2017.06.022-
dcterms.abstract建立磁流变阻尼器的动态模型以描述其强非线性动力学行为是智能磁流变控制系统设计及应用的关键环节之一。泛化能力是衡量基于人工神经网络技术的磁流变阻尼器非参数化模型性能的重要指标,也是保证控制系统稳定性和可靠性的重要因素。基于磁流变阻尼器的动力学试验数据,提出贝叶斯推理分析框架下的非线性自回归(nonlinear autoregressive with exogenous inputs,NARX)神经网络技术建立磁流变阻尼器的动态模型,通过网络结构优化和正则化学习算法的结合以有效地提高模型的预测精度和泛化能力。研究结果表明,基于贝叶斯推理的NARX网络模型能够准确地预测磁流变阻尼器在周期和随机激励下的非线性动态行为,同时验证了该模型相比于非正则化模型在泛化性能方面的优越性,因此,有利于实现磁流变控制系统的实时、鲁棒智能化控制。-
dcterms.abstractThe dynamic modeling for magnetorheological (MR) dampers to describe their highly nonlinear dynamic characteristics is essential for the design and implementation of a smart MR control system. One critical concern in constructing a nonparametric MR damper model by employing the artificial neural network technique is its generalization capability, which is also significant to guarantee the stability and reliability of the MR control system. The paper presents the modeling of MR dampers with the employment of the NARX (nonlinear autoregressive with exogenous inputs) network technique within a Bayesian inference framework, and addresses the enhancement of model prediction accuracy and generalization capability in terms of the network architecture optimization and regularized network learning algorithm. The Bayesian regularized NARX network model for the MR damper is demonstrated to outperform the non-regularized network model with the superior prediction and generalization performance in the scenarios of harmonic and random excitations. Therefore, the proposed model with enhanced generalization is beneficial to realize the real-time and robust smart control of MR systems.-
dcterms.accessRightsopen accessen_US
dcterms.alternative磁流变阻尼器非参数化模型泛化能力的提高-
dcterms.bibliographicCitation振动与冲击 (Journal of vibration and shock), 2017, v. 36, no. 6, p. 146-151 and 167-
dcterms.isPartOf振动与冲击 (Journal of vibration and shock)-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85020198392-
dc.description.validate201802 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Chen_Enhanced_Generalization_nonparametric.pdf569.7 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

211
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

37
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

4
Last Week
0
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.