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Structural Monitoring and Maintenance
  Volume 1, Number 4, December 2014, pages 427-449
open access

Hybrid evolutionary identification of output-error state-space models
Vasilis K. Dertimanis, Eleni N. Chatzi and Minas D. Spiridonakos

    A hybrid optimization method for the identification of state–space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.
Key Words
    Evolution strategies; Levenberg-Marquardt; hybrid optimization; system identification;state-space; modal analysis
Vasilis K. Dertimanis, Eleni N. Chatzi and Minas D. Spiridonakos: Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering,
ETH Zürich, Stefano–Franscini–Platz 5, 8093 Zürich, Switzerland

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