The Effect of Measurement Distribution on System Identification and Detection of Behavior of Nonlinearities of Data
Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri
Abstract:
In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.
Keywords: Gaussian process, Nonlinearity distribution, Particle filter.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099148
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1722References:
[1] E-W Bai, “A blind approach to Hammerstein-Wiener model identification,” Automatica, 38(6):967–979, 2002.
[2] Johan Dahlin, Fredrik Lindsten and Thomas B. Schön, “Particle Metropolis Hastings using gradient and Hessian information”. Statistics and Computing, 2014. (accepted for publication)
[3] Johan Dahlin, Fredrik Lindsten and Thomas B. Schön, “Second-order Particle MCMC for Bayesian parameter inference, in Proceedings of the 18th” World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 2014.
[4] Johan Dahlin, Fredrik Lindsten and Thomas B. Schön,” Particle Metropolis Hastings using Langevin dynamics”, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013.
[5] G. Pillonetto and A. Chiuso, “Gaussian processes for Wiener- Hammerstein system identification”. In Proceedings of the 15th IFAC Symposium on System Identification, Saint-Malo, France.
[6] Fredrik Lindsten, Thomas B. Schön and Michael I. Jordan,” A semiparametric Bayesian approach to Wiener system identification”, in Proceedings of the 16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, July 2012
[7] Johan Dahlin, Fredrik Lindsten, Thomas B. Schön and Adrian Wills, “Hierarchical Bayesian ARX models for robust inference”, in Proceedings of the 16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, July 2012.
[8] T. Choi and M. J. Schervish, “On posterior consistency in nonparametric regression problems”. Journal of Multivariate Analysis, 98(10):1969–1987, 2007.
[9] Farhad Asadi, Mohammad javad Mollakazemi, “Investigation of fluctuation locations and effect of data distribution in time series dynamical regimes”.. Accepted and oral presentation in ICBCBBE 2014: XII International Conference on Bioinformatics, Computational Biology and Biomedical Engineering, October, 27-28, 2014, Istanbul, turkey.
[10] Farhad Asadi, Mohammad javad Mollakazemi, Aref Ghafouri, “The influence of parameters of modeling and data distribution for optimal condition on locally weighted projection regression method”.. Accepted and oral presentation in ICMSE 2014: XII International Conference on Mathematics and Statistical Engineering, October, 27-28, 2014, Istanbul, turkey.
[11] Mohammad javad Mollakazemi, Farhad Asadi, “Real-time adaptive obstacle avoidance in dynamic environments with different D-S”.. Accepted and oral presentation in ICARM 2014: XII International Conference on Automation, Robotics and Mechatronics, October, 27-28, 2014, Istanbul, turkey.