Multi-objective differential evolution algorithm for stochastic system identification

Zhou Jin, Akira Mita, Li Rongshuai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The last decade has witnessed rapid developments in structural system identification methodologies based on intelligent algorithms, which are formulated as multi-modal optimization problems. However, these deterministic methods more or less ignore uncertainties, such as modeling errors and measurement errors, that are inevitably involved in the system identification problem of civil-engineering structures. A new stochastic structural identification method is proposed that takes into account parametric uncertainties in the parameters of building structures. The proposed method merges the advantages of the multi-objective differential evolution optimization algorithm for the non-domination selection strategy and the probability density evolution method for incorporating parametric uncertainties. The results of simulations on identifying the unknown parameters of a structural system demonstrate the feasibility and effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume8692
DOIs
Publication statusPublished - 2013
Event2013 SPIE Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013 - San Diego, CA, United States
Duration: 2013 Mar 102013 Mar 14

Other

Other2013 SPIE Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
CountryUnited States
CitySan Diego, CA
Period13/3/1013/3/14

Fingerprint

Stochastic systems
system identification
Differential Evolution Algorithm
System Identification
Stochastic Systems
Identification (control systems)
optimization
Structural Identification
Parametric Uncertainty
engineering
methodology
Civil engineering
Measurement errors
Multimodal Optimization
Civil Engineering
Modeling Error
Identification Problem
Probability Density
simulation
Measurement Error

Keywords

  • Multi-objective optimization
  • Stochastic dynamic system
  • Structural system identification

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Jin, Z., Mita, A., & Rongshuai, L. (2013). Multi-objective differential evolution algorithm for stochastic system identification. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 8692). [86923G] https://doi.org/10.1117/12.2006578

Multi-objective differential evolution algorithm for stochastic system identification. / Jin, Zhou; Mita, Akira; Rongshuai, Li.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 8692 2013. 86923G.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jin, Z, Mita, A & Rongshuai, L 2013, Multi-objective differential evolution algorithm for stochastic system identification. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 8692, 86923G, 2013 SPIE Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, San Diego, CA, United States, 13/3/10. https://doi.org/10.1117/12.2006578
Jin Z, Mita A, Rongshuai L. Multi-objective differential evolution algorithm for stochastic system identification. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 8692. 2013. 86923G https://doi.org/10.1117/12.2006578
Jin, Zhou ; Mita, Akira ; Rongshuai, Li. / Multi-objective differential evolution algorithm for stochastic system identification. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 8692 2013.
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