Particle-based Gaussian process optimization for input design in nonlinear dynamical models
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Johan Dahlin | Thomas B. Schön | Cristian R. Rojas | Patricio E. Valenzuela | P. E. Valenzuela | Thomas Bo Schön | C. Rojas | J. Dahlin
[1] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[2] F. Lindsten,et al. Particle Filter-Based Gaussian Process Optimisation for Parameter Inference , 2013, 1311.0689.
[3] Graham C. Goodwin,et al. Robust optimal experiment design for system identification , 2007, Autom..
[4] Marco Forgione,et al. Experiment design for parameter estimation in nonlinear systems based on multilevel excitation , 2014, 2014 European Control Conference (ECC).
[5] Bradley P. Carlin,et al. BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..
[6] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[7] Michael A. Osborne,et al. Gaussian Processes for Global Optimization , 2008 .
[8] A. Doucet,et al. A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .
[9] Fredrik Lindsten,et al. Backward Simulation Methods for Monte Carlo Statistical Inference , 2013, Found. Trends Mach. Learn..
[10] Håkan Hjalmarsson,et al. Input design via LMIs admitting frequency-wise model specifications in confidence regions , 2005, IEEE Transactions on Automatic Control.
[11] Thomas Bo Schön,et al. Input Design for Nonlinear Stochastic Dynamic Systems – A Particle Filter Approach , 2011 .
[12] Håkan Hjalmarsson,et al. A graph theoretical approach to input design for identification of nonlinear dynamical models , 2015, Autom..
[13] Håkan Hjalmarsson,et al. Identification of ARX systems with non-stationary inputs - asymptotic analysis with application to adaptive input design , 2009, Autom..
[14] P. E. Valenzuela,et al. A graph/particle-based method for experiment design in nonlinear systems , 2014, 1403.4044.
[15] Aurélien Garivier,et al. Sequential Monte Carlo smoothing for general state space hidden Markov models , 2011, 1202.2945.
[16] David R. Cox. Planning of Experiments , 1958 .
[17] P. Billingsley,et al. Convergence of Probability Measures , 1970, The Mathematical Gazette.
[18] Johan Dahlin,et al. On robust input design for nonlinear dynamical models , 2017, Autom..
[19] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[20] Phillip Boyle,et al. Gaussian Processes for Regression and Optimisation , 2007 .
[21] Alex Simpkins,et al. System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.
[22] R. Steele. Optimization , 2005 .
[23] E. Weinstein,et al. A new method for evaluating the log-likelihood gradient, the Hessian, and the Fisher information matrix for linear dynamic systems , 1989, IEEE Trans. Inf. Theory.
[24] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[25] D.G. Dudley,et al. Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.
[26] Håkan Hjalmarsson,et al. On optimal input design for nonlinear FIR-type systems , 2010, 49th IEEE Conference on Decision and Control (CDC).
[27] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[28] Johan Schoukens,et al. A preliminary study on optimal input design for nonlinear systems , 2013, 52nd IEEE Conference on Decision and Control.