A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor
暂无分享,去创建一个
[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] R. Shumway,et al. AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .
[3] D. Rubin. Using the SIR algorithm to simulate posterior distributions , 1988 .
[4] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[5] G. Evensen. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .
[6] Dale E. Seborg,et al. Nonlinear Process Control , 1996 .
[7] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[8] Hugh F. Durrant-Whyte,et al. A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..
[9] Rudolph van der Merwe,et al. The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).
[10] S. Haykin. Kalman Filtering and Neural Networks , 2001 .
[11] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[12] Christophe Andrieu,et al. Online expectation-maximization type algorithms for parameter estimation in general state space models , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[13] A. Doucet,et al. Parameter estimation in general state-space models using particle methods , 2003 .
[14] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[15] Christophe Andrieu,et al. Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.
[16] Rolf Johan Lorentzen,et al. Analysis of the ensemble Kalman filter for estimation of permeability and porosity in reservoir models , 2005 .
[17] Elaine Martin,et al. Particle filters for state and parameter estimation in batch processes , 2005 .
[18] D. Stoffer,et al. Parameter estimation in stochastic volatility models with missing data using particle methods and the em algorithm , 2005 .
[19] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[20] G. Evensen. Data Assimilation: The Ensemble Kalman Filter , 2006 .
[21] Thomas B. Schön,et al. Parameter Estimation for Discrete-Time Nonlinear Systems Using EM , 2008 .
[22] Simo Särkkä,et al. Unscented Rauch-Tung-Striebel Smoother , 2008, IEEE Trans. Autom. Control..
[23] Giorgio Battistelli,et al. Unfalsified Virtual Reference Adaptive Switching Control of Plants with Persistent Disturbances , 2008 .
[24] R. B. Gopaluni. Identification of Nonlinear Processes with known Model Structure Under Missing Observations , 2008 .
[25] R. B. Gopaluni. A particle filter approach to identification of nonlinear processes under missing observations , 2008 .
[26] Geir Evensen. Estimation in an oil reservoir simulator , 2009 .
[27] Y. Bar-Shalom,et al. The probabilistic data association filter , 2009, IEEE Control Systems.
[28] Sirish L. Shah,et al. Comparison of Expectation-Maximization based parameter estimation using Particle Filter, Unscented and Extended Kalman Filtering techniques , 2009 .
[29] Arnaud Doucet,et al. An overview of sequential Monte Carlo methods for parameter estimation in general state-space models , 2009 .
[30] G. Evensen. The ensemble Kalman filter for combined state and parameter estimation , 2009, IEEE Control Systems.
[31] Alexandra Seiler,et al. Advanced Reservoir Management Workflow Using an EnKF Based Assisted History Matching Method , 2009 .
[32] R. Bhushan Gopaluni,et al. Nonlinear system identification under missing observations: The case of unknown model structure , 2010 .