On the Performance of Online Parameter Estimation Algorithms in Systems with Various Identifiability Properties

@inproceedings{Olivier2017OnTP,
  title={On the Performance of Online Parameter Estimation Algorithms in Systems with Various Identifiability Properties},
  author={Audrey Olivier and Andrew W. Smyth},
  booktitle={Front. Built Environ.},
  year={2017}
}
In recent years, Bayesian inference has been extensively used for parameter estimation in non-linear systems; in particular, it has proved to be very useful for damage detection purposes. The problem of parameter estimation is inherently correlated with the issue of identifiability, i.e., is one able to learn uniquely the parameters of the system from available measurements? The identifiability properties of the system will govern the complexity of the posterior probability density functions… CONTINUE READING