The analysis of random systems with combined parametric and non-parametric uncertainty models

被引:0
|
作者
Cicirello, A. [1 ]
Langley, R. S. [1 ]
机构
[1] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England
关键词
RESPONSE VARIANCE PREDICTION; RECIPROCITY RELATIONSHIP; ENERGY; STATISTICS;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In recent years there have been advances in the use of non-parametric models of uncertainty to predict the response statistics of random structures. With this approach, it is assumed that various components of the system are sufficiently random for universal statistical distributions to be applied directly to the natural frequencies and mode shapes, thus obviating the need for a detailed description of the underlying random physical parameters. A hybrid finite element (FE)/statistical energy analysis (SEA) method has previously been developed based on this approach, in which components are considered to be either deterministic or highly random. In the present paper this method is extended by applying parametric uncertainty models to components that are not highly random. Both probabilistic and possibilistic models are considered, and the method is illustrated by application to an example built-up plate system.
引用
收藏
页码:1997 / 2009
页数:13
相关论文
共 50 条
  • [41] Explaining the volatility smile: Non-parametric versus parametric option models
    Lin H.-C.
    Chen R.-R.
    Palmon O.
    Review of Quantitative Finance and Accounting, 2016, 46 (4) : 907 - 935
  • [42] Unifying Framework for Decomposition Models of Parametric and Non-parametric Image Registration
    Ibrahim, Mazlinda
    Chen, Ke
    PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM24): MATHEMATICAL SCIENCES EXPLORATION FOR THE UNIVERSAL PRESERVATION, 2017, 1870
  • [43] Non-parametric identifiability and sensitivity analysis of synthetic control models
    Zeitler, Jakob
    Vlontzos, Athanasios
    Gilligan-Lee, Ciaran M.
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 213, 2023, 213 : 850 - 865
  • [44] Parametric and Non-parametric Encompassing Procedures
    Bontemps, Christophe
    Florens, Jean-Pierre
    Richard, Jean-Francois
    OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2008, 70 : 751 - 780
  • [45] Robust Wiener filtering with non-parametric spectral uncertainty
    Correa, G. O.
    Freire, E. S.
    INTERNATIONAL JOURNAL OF CONTROL, 2009, 82 (12) : 2311 - 2326
  • [46] Adaptive Learning Control for Nonlinear Systems With Parametric and Non-Parametric Uncertainties
    Sun, Yunping
    Xu, Tianwei
    Xia, Youming
    Xiao, Fei
    PROCEEDINGS OF 2010 ASIA-PACIFIC YOUTH CONFERENCE ON COMMUNICATION, VOLS 1 AND 2, 2010, : 315 - 320
  • [47] Non-Parametric Method for Diagnosis in Technical Systems Described by Linear Models
    Zhirabok, Alexey
    Pavlov, Sergey
    Shumsky, Alexey
    PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, : 429 - 434
  • [48] Non-parametric Models for Non-negative Functions
    Marteau-Ferey, Ulysse
    Bach, Francis
    Rudi, Alessandro
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [49] Non-parametric identification of generalized Hammerstein models
    Emara-Shabaik, HE
    Moustafa, KAF
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1998, 29 (01) : 91 - 93
  • [50] Non-parametric habitat models with automatic interactions
    McCune, Bruce
    JOURNAL OF VEGETATION SCIENCE, 2006, 17 (06) : 819 - 830