On the identification of hysteretic systems. Part II: Bayesian sensitivity analysis and parameter confidence

This paper forms the second in a short sequence considering the system identification problem for hysteretic systems. The basic model for parameter estimation is assumed to be the Bouc–Wen model as this has proved particularly versatile in the past. Previous work on the Bouc–Wen system has shown that the system response is more sensitive to some parameters than others and that the errors in the associated parameter estimates vary as a consequence. The first objective of the current paper is to demonstrate the use of a principled Bayesian approach to parameter sensitivity analysis for the Bouc–Wen system. The approach is based on Gaussian process emulation and is encoded in the software package GEM-SA. The paper considers a five-parameter Bouc–Wen model, and the sensitivity analysis is based on data generated by computer simulation of a single-degree-of-freedom system. The second major objective of the paper is also concerned with uncertainty analysis and considers the problem of obtaining estimates of parameter confidence intervals from optimisation-based system identification schemes. Two different estimators of the parameter covariance matrix are demonstrated and the results are compared with those from an independent MCMC (Markov Chain Monte Carlo) identification method.

► Bayesian sensitivity analysis of hysteretic systems.
► Determination of confidence intervals for parameter estimates from evolutionary schemes.
► Comparison of results with MCMC identification scheme which also yields confidence intervals.