The Effect of Measurement Distribution on System Identification and Detection of Behavior of Nonlinearities of Data

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.

• Mohammad Javad Mollakazemi
• Farhad Asadi
• Aref Ghafouri

• Gaussian process
• Nonlinearity distribution
• Particle filter.

[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.