Publikationer av Cristian Rojas
Refereegranskade
Artiklar
[1]
P. L. Wachel, K. Kowalczyk och C. R. Rojas, "Decentralized diffusion-based learning under non-parametric limited prior knowledge," European Journal of Control, vol. 75, 2024.
[2]
T. Oomen och C. R. Rojas, "Reset-free data-driven gain estimation: Power iteration using reversed-circulant matrices," Automatica, vol. 161, 2024.
[3]
J. Parsa, C. R. Rojas och H. Hjalmarsson, "Application-Oriented Input Design With Low Coherence Constraint," IEEE Control Systems Letters, vol. 7, s. 193-198, 2023.
[4]
J. Parsa, C. R. Rojas och H. Hjalmarsson, "Coherence-Based Input Design for Nonlinear Systems," IEEE Control Systems Letters, vol. 7, s. 2934-2939, 2023.
[5]
R. A. González et al., "On the Relation Between Discrete and Continuous-Time Refined Instrumental Variable Methods," IEEE Control Systems Letters, vol. 7, s. 2233-2238, 2023.
[6]
S. Pan et al., "Consistency analysis and bias elimination of the Instrumental-Variable-based State Variable Filter method," Automatica, vol. 144, 2022.
[7]
C. R. Rojas och P. Wachel, "On State-Space Representations of General Discrete-Time Dynamical Systems," IEEE Transactions on Automatic Control, vol. 67, no. 12, s. 6975-6979, 2022.
[8]
R. Mochaourab et al., "Post Hoc Explainability for Time Series Classification Toward a signal processing perspective," IEEE signal processing magazine (Print), vol. 39, no. 4, s. 119-129, 2022.
[9]
R. A. González et al., "Refined instrumental variable methods for unstable continuous-time systems in closed-loop," International Journal of Control, 2022.
[10]
M. I. Müller och C. R. Rojas, "Risk-theoretic optimal design of output-feedback controllers via iterative convex relaxations," Automatica, vol. 136, s. 110042-110042, 2022.
[11]
R. A. González et al., "Theoretical and practical aspects of the convergence of the SRIVC estimator for over-parameterized models," Automatica, vol. 142, s. 110355-110355, 2022.
[12]
R. A. González et al., "Consistent identification of continuous-time systems under multisine input signal excitation," Automatica, vol. 133, 2021.
[13]
B. Djehiche, O. Mazhar och C. R. Rojas, "Finite impulse response models : A non-asymptotic analysis of the least squares estimator," Bernoulli, vol. 27, no. 2, s. 976-1000, 2021.
[14]
I. Lourenço et al., "Hidden Markov Models : Inverse Filtering, Belief Estimation and Privacy Protection," Journal of Systems Science and Complexity, vol. 34, no. 5, s. 1801-1820, 2021.
[15]
S. Pan et al., "Consistency analysis of the Simplified Refined Instrumental Variable method for Continuous-time systems," Automatica, vol. 113, 2020.
[16]
S. Pan et al., "Efficiency analysis of the Simplified Refined Instrumental Variable method for Continuous-time systems," Automatica, vol. 121, 2020.
[17]
R. Mattila et al., "Inverse Filtering for Hidden Markov Models With Applications to Counter-Adversarial Autonomous Systems," IEEE Transactions on Signal Processing, vol. 68, s. 4987-5002, 2020.
[18]
R. Mattila et al., "Estimating Private Beliefs of Bayesian Agents Based on Observed Decisions," IEEE Control Systems Letters, s. 523-528, 2019.
[19]
M. Galrinho, C. R. Rojas och H. Hjalmarsson, "Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting," Automatica, vol. 102, s. 45-57, 2019.
[20]
P. E. Valenzuela, T. B. Schön och C. R. Rojas, "On model order priors for Bayesian identification of SISO linear systems," International Journal of Control, vol. 92, no. 7, s. 1645-1661, 2019.
[21]
M. Galrinho, C. R. Rojas och H. Hjalmarsson, "Parametric Identification Using Weighted Null-Space Fitting," IEEE Transactions on Automatic Control, vol. 64, no. 7, s. 2798-2813, 2019.
[22]
H. Kwon et al., "Three-harmonic optimal multisine input power spectrum for bioimpedance identification," Physiological Measurement, vol. 40, no. 5, 2019.
[23]
M. Sadeghi, C. R. Rojas och B. Wahlberg, "A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization," IFAC-PapersOnLine, vol. 51, no. 15, s. 96-101, 2018.
[24]
R. A. González och C. R. Rojas, "A fully Bayesian approach to kernel-based regularization for impulse response estimation⁎," IFAC-PapersOnLine, vol. 51, no. 15, s. 186-191, 2018.
[25]
H. Ha et al., "An analysis of the SPARSEVA estimate for the finite sample data case," Automatica, vol. 96, s. 141-149, 2018.
[26]
P. E. Valenzuela, C. R. Rojas och H. Hjalmarsson, "Analysis of averages over distributions of Markov processes," Automatica, vol. 98, s. 354-357, 2018.
[27]
M. . R. Abdalmoaty, C. R. Rojas och H. Hjalmarsson, "Identification of a Class of Nonlinear Dynamical Networks⁎," IFAC-PapersOnLine, vol. 51, no. 15, s. 868-873, 2018.
[28]
J. Bjurgert, P. E. Valenzuela och C. R. Rojas, "On Adaptive Boosting for System Identification," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, s. 4510-4514, 2018.
[29]
R. Mattila et al., "Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models," IEEE Signal Processing Letters, vol. 24, no. 12, s. 1813-1817, 2017.
[30]
R. Mattila et al., "Computing monotone policies for Markov decision processes : a nearly-isotonic penalty approach," IFAC-PapersOnLine, vol. 50, no. 1, s. 8429-8434, 2017.
[31]
D. Eckhard et al., "Cost function shaping of the output error criterion," Automatica, vol. 76, s. 53-60, 2017.
[32]
A. Ebadat et al., "Model Predictive Control oriented experiment design for system identification : A graph theoretical approach," Journal of Process Control, vol. 52, s. 75-84, 2017.
[33]
P. E. Valenzuela et al., "On robust input design for nonlinear dynamical models," Automatica, vol. 77, s. 268-278, 2017.
[34]
O. Trollberg, C. Rojas och E. W. Jacobsen, "Online constraint adaptation in economic model predictive control," IFAC-PapersOnLine, vol. 50, no. 1, s. 9065-9070, 2017.
[35]
R. A. González et al., "Optimal enforcement of causality in non-parametric transfer function estimation," IEEE Control Systems Letters, vol. 1, no. 2, s. 268-273, 2017.
[36]
T. Oomen och C. R. Rojas, "Sparse iterative learning control with application to a wafer stage : Achieving performance, resource efficiency, and task flexibility," Mechatronics (Oxford), vol. 47, s. 134-147, 2017.
[37]
N. Everitt et al., "Variance analysis of linear SIMO models with spatially correlated noise," Automatica, vol. 77, s. 68-81, 2017.
[38]
M. Malek Mohammadi, C. Rojas och B. Wahlberg, "A Class of Nonconvex Penalties Preserving OverallConvexity in Optimization-Based Mean Filtering," IEEE Transactions on Signal Processing, vol. 65, no. 24, s. 6650-6664, 2016.
[39]
J. Ottersten, B. Wahlberg och C. R. Rojas, "Accurate Changing Point Detection for l(1) Mean Filtering," IEEE Signal Processing Letters, vol. 23, no. 2, s. 297-301, 2016.
[40]
L. Özkan et al., "Advanced autonomous model-based operation of industrial process systems (Autoprofit) : Technological developments and future perspectives," ANNUAL REVIEWS IN CONTROL, vol. 42, s. 126-142, 2016.
[41]
K. Li et al., "Alternating strategies with internal ADMM for low-rank matrix reconstruction," Signal Processing, vol. 121, s. 153-159, 2016.
[42]
C. A. Larsson et al., "An application-oriented approach to dual control with excitation for closed-loop identification," European Journal of Control, vol. 29, s. 1-16, 2016.
[43]
M. Sundin et al., "Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction," IEEE Transactions on Signal Processing, vol. 64, no. 20, s. 5327-5339, 2016.
[44]
M. Malek-Mohammadi et al., "Successive Concave Sparsity Approximation for Compressed Sensing," IEEE Transactions on Signal Processing, vol. 64, no. 21, s. 5657-5671, 2016.
[45]
M. Malek Mohammadi et al., "Upper bounds on the error of sparse vector and low-rank matrix recovery," Signal Processing, vol. 120, s. 249-254, 2016.
[46]
C. R. Rojas, P. E. Valenzuela och R. A. Rojas, "A Critical View on Benchmarks based on Randomly Generated Systems," IFAC-PapersOnLine, vol. 48, no. 28, s. 1471-1476, 2015.
[47]
P. E. Valenzuela, C. R. Rojas och H. Hjalmarsson, "A graph theoretical approach to input design for identification of nonlinear dynamical models," Automatica, vol. 51, s. 233-242, 2015.
[48]
D. Katselis och C. R. Rojas, "Application-Oriented Estimator Selection," IEEE Signal Processing Letters, vol. 22, no. 4, s. 489-493, 2015.
[49]
R. Mattila, C. R. Rojas och B. Wahlberg, "Evaluation of Spectral Learning for the Identification of Hidden Markov Models," IFAC-PapersOnLine, vol. 48, no. 28, s. 897-902, 2015.
[50]
C. A. Larsson et al., "Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer," Journal of Process Control, vol. 31, s. 1-16, 2015.
[51]
N. Everitt et al., "On the Effect of Noise Correlation in Parameter Identification of SIMO Systems," IFAC-PapersOnLine, vol. 48, no. 28, s. 326-331, 2015.
[52]
D. Katselis et al., "On the end-performance metric estimator selection," Automatica, vol. 58, s. 22-27, 2015.
[53]
N. Blomberg, C. R. Rojas och B. Wahlberg, "Regularization Paths for Re-Weighted Nuclear Norm Minimization," IEEE Signal Processing Letters, vol. 22, no. 11, s. 1980-1984, 2015.
[54]
H. Ha et al., "Reweighted nuclear norm regularization : A SPARSEVA approach," IFAC-PapersOnLine, vol. 48, no. 28, s. 1172-1177, 2015.
[55]
T. Oomen et al., "Iterative Data-Driven H-infinity Norm Estimation of Multivariable Systems With Application to Robust Active Vibration Isolation," IEEE Transactions on Control Systems Technology, vol. 22, no. 6, s. 2247-2260, 2014.
[56]
V. Krishnamurthy och C. R. Rojas, "Reduced Complexity HMM Filtering With Stochastic Dominance Bounds : A Convex Optimization Approach," IEEE Transactions on Signal Processing, vol. 62, no. 23, s. 6309-6322, 2014.
[57]
B. Sanchez och C. R. Rojas, "Robust excitation power spectrum design for broadband impedance spectroscopy," Measurement science and technology, vol. 25, no. 6, s. 065501, 2014.
[58]
C. R. Rojas, R. Toth och H. Hjalmarsson, "Sparse Estimation of Polynomial and Rational Dynamical Models," IEEE Transactions on Automatic Control, vol. 59, no. 11, s. 2962-2977, 2014.
[59]
C. R. Rojas, D. Katselis och H. Hjalmarsson, "A Note on the SPICE Method," IEEE Transactions on Signal Processing, vol. 61, no. 18, s. 4545-4551, 2013.
[60]
D. Katselis et al., "Frequency smoothing gains in preamble-based channel estimation for multicarrier systems," Signal Processing, vol. 93, no. 9, s. 2777-2782, 2013.
[61]
D. Eckhard et al., "Input design as a tool to improve the convergence of PEM," Automatica, vol. 49, no. 11, s. 3282-3291, 2013.
[62]
D. Katselis et al., "Training sequence design for MIMO channels: an application-oriented approach," EURASIP Journal on Wireless Communications and Networking, vol. 2013, s. 245, 2013.
[63]
J. C. Agüero et al., "Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation," Automatica, vol. 48, no. 4, s. 632-637, 2012.
[64]
C. R. Rojas et al., "Analyzing iterations in identification with application to nonparametric H-infinity-norm estimation," Automatica, vol. 48, no. 11, s. 2776-2790, 2012.
[65]
C. Mueller, C. R. Rojas och G. C. Goodwin, "Generation of amplitude constrained signals with a prescribed spectrum," Automatica, vol. 48, no. 1, s. 153-158, 2012.
[66]
B. Sanchez et al., "On the calculation of the D-optimal multisine excitation power spectrum for broadband impedance spectroscopy measurements," Measurement science and technology, vol. 23, no. 8, s. 085702, 2012.
[67]
C. R. Rojas et al., "Robustness in Experiment Design," IEEE Transactions on Automatic Control, vol. 57, no. 4, s. 860-874, 2012.
[68]
C. R. Rojas et al., "An adaptive method for consistent estimation of real-valued non-minimum phase zeros in stable LTI systems," Automatica, vol. 47, no. 7, s. 1388-1398, 2011.
[69]
A. Esparza et al., "Asymptotic statistical analysis for model-based control design strategies," Automatica, vol. 47, no. 5, s. 1041-1046, 2011.
[70]
J. Mårtensson, C. R. Rojas och H. Hjalmarsson, "Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller," Automatica, vol. 47, no. 3, s. 578-583, 2011.
[71]
H. Hjalmarsson et al., "On the accuracy in errors-in-variables identification compared to prediction-error identification," Automatica, vol. 47, no. 12, s. 2704-2712, 2011.
[72]
K. E. J. Olofsson et al., "Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response," Plasma Physics and Controlled Fusion, vol. 53, no. 8, s. 084003, 2011.
[73]
C. R. Rojas et al., "The cost of complexity in system identification: The Output Error case," Automatica, vol. 47, no. 9, s. 1938-1948, 2011.
[74]
C. R. Rojas, P. Zetterberg och P. Händel, "Transceiver Inphase/Quadrature Imbalance, Ellipse Fitting, and the Universal Software Radio Peripheral," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 11, s. 3629-3639, 2011.
[75]
J. A. Ramírez et al., "Aportes a la Teoría y la Implementación del Método LSCR," RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, vol. 7, no. 3, s. 83-94, 2010.
[76]
C. R. Rojas et al., "The Cost of Complexity in System Identification: Frequency Function Estimation of Finite Impulse Response Systems," IEEE Transactions on Automatic Control, vol. 55, no. 10, s. 2298-2309, 2010.
[77]
C. R. Rojas, J. S. Welsh och J. C. Agüero, "Fundamental Limitations on the Variance of Estimated Parametric Models," IEEE Transactions on Automatic Control, vol. 54, no. 5, s. 1077-1081, 2009.
[78]
C. R. Rojas et al., "On the Equivalence of Least Costly and Traditional Experiment Design for Control," Automatica, vol. 44, no. 11, s. 2706-2715, 2008.
[79]
G. C. Goodwin et al., "Robust Identification of Process Models from Plant Data," Journal of Process Control, vol. 18, no. 9, s. 810-820, 2008.
[80]
C. R. Rojas et al., "Robust Optimal Experiment Design for System Identification," Automatica, vol. 43, no. 6, s. 993-1008, 2007.
[81]
C. R. Rojas, R. A. Rojas och M. E. Salgado, "Equivalence between Transfer-Matrix and Observed-State Feedback Control," IEE Proceedings - Control Theory and Applications, vol. 153, no. 2, s. 147-155, 2006.
Konferensbidrag
[82]
B. Lakshminarayanan och C. R. Rojas, "A Unified Approach to Differentially Private Bayes Point Estimation," i 22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023, 2023, s. 8375-8380.
[83]
R. A. González et al., "An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification," i IFAC-PapersOnLine, 2023, s. 4204-4209.
[84]
A. Elton et al., "Blind Nonparametric Estimation of SISO Continuous-time Systems," i IFAC-PapersOnLine, 2023, s. 4222-4227.
[85]
I. Lourenço et al., "Diagnosing and Repairing Feature Representations Under Distribution Shifts," i 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, s. 3638-3645.
[86]
B. Lakshminarayanan och C. R. Rojas, "Minimax Two-Stage Gradient Boosting for Parameter Estimation," i 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, s. 1189-1194.
[87]
R. Winqvist et al., "Optimal Transport for Correctional Learning," i 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, s. 6806-6812.
[88]
A. Elton et al., "Parametric Continuous-Time Blind System Identification," i 2023 62nd IEEE Conference on Decision and Control, CDC2023, 2023, s. 1474-1479.
[89]
R. A. González et al., "Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach," i 22nd IFAC World CongressYokohama, Japan, July 9-14, 2023, 2023, s. 4216-4221.
[90]
B. Lakshminarayanan och C. R. Rojas, "A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation," i 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, s. 5369-5374.
[91]
I. Lourenço et al., "A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning," i 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, s. 3456-3461.
[92]
J. Parsa, C. R. Rojas och H. Hjalmarsson, "Optimal Input Design for Sparse System Identification," i 2022 EUROPEAN CONTROL CONFERENCE (ECC), 2022, s. 1999-2004.
[93]
I. Lourenço et al., "Cooperative System Identification via Correctional Learning," i IFAC PAPERSONLINE, 2021, s. 19-24.
[94]
M. I. Müller et al., "Data-Driven Input-Passivity Estimation Using Power Iterations," i IFAC PAPERSONLINE, 2021, s. 619-624.
[95]
R. A. González, C. R. Rojas och H. Hjalmarsson, "Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations," i Proceedings 2021 60th IEEE conference on decision and control (CDC), 2021, s. 114-119.
[96]
R. A. González et al., "The SRIVC algorithm for continuous-time system identification with arbitrary input excitation in open and closed loop," i Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021.
[97]
R. A. González och C. R. Rojas, "A Finite-Sample Deviation Bound for Stable Autoregressive Processes," i A Finite-Sample Deviation Bound for Stable Autoregressive Processes, 2020, s. 1-10.
[98]
R. A. González, W. James S. och C. R. Rojas, "Enforcing stability through ellipsoidal inner approximations in the indirect approach for continuous-time system identification," i 21st IFAC World Congress (IFAC 2020), Berlin, Germany, 2020, s. 566-571.
[99]
R. Mattila et al., "Fast and consistent learning of hidden markov models by incorporating non-consecutive correlations," i 37th International Conference on Machine Learning, ICML 2020, 2020, s. 6741-6752.
[100]
R. A. González och C. R. Rojas, "Finite sample deviation and variance bounds for first order autoregressive processes," i 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020, s. 5380-5384.
[101]
I. Lourenço et al., "How to Protect Your Privacy? : A Framework for Counter-Adversarial Decision Making," i Proceedings of the IEEE Conference on Decision and Control, 2020, s. 1785-1791.
[102]
M. I. Müller och C. R. Rojas, "Iterative H-infinity-norm Estimation Using Cyclic-Prefixed Signals," i 2020 59Th IEEE Conference On Decision And Control (Cdc), 2020, s. 2869-2874.
[103]
M. Müller och C. R. Rojas, "Iterative H∞-norm estimation using cyclic-prefixed signals," i 59th IEEE Conference on Decision and Control, 2020.
[104]
R. Mattila et al., "What did your adversary believe? : Optimal filtering and smoothing in counter-adversarial autonomous systems," i 2020 IEEE international conference on acoustics, speech, and signal processing, 2020, s. 5495-5499.
[105]
M. I. Müller och C. R. Rojas, "Gain estimation of linear dynamical systems using Thompson Sampling," i Proceedings of Machine Learning Research, 2019, s. 1535-1543.
[106]
M. I. Müller och C. R. Rojas, "Risk-Coherent H∞-optimal Filter Design Under Model Uncertainty with Applications to MISO Control," i 2019 18th European Control Conference, ECC 2019, 2019, s. 1461-1466.
[107]
M. I. Müller och C. R. Rojas, "A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems," i IFAC-PapersOnLine, 2018, s. 84-89.
[108]
M. I. Müller et al., "A Risk-Theoretical Approach to H2-Optimal Control under Covert Attacks," i 57th IEEE Conference on Decision and Control, 2018, s. 4553-4558.
[109]
R. A. Gonzalez, C. R. Rojas och J. S. Welsh, "An asymptotically optimal indirect approach to continuous-time system identification," i 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, s. 638-643.
[110]
O. Mazhar et al., "Bayesian model selection for change point detection and clustering," i 35th International Conference on Machine Learning, ICML 2018, 2018, s. 5497-5520.
[111]
M. Abdalmoaty, C. R. Rojas och H. Hjalmarsson, "Identication of a Class of Nonlinear Dynamical Networks," i 18th IFAC Symposium on System Identification, 2018.
[112]
R. Mattila et al., "Inverse Filtering for Linear Gaussian State-Space Models," i Proceedings of the 57th IEEE Conference on Decision and Control (CDC’18), Miami Beach, FL, USA, 2018., 2018.
[113]
O. Trollberg et al., "On optimization of paper machines using economic model predictive control," i Paper Conference and Trade Show, PaperCon 2018, 2018, s. 286-293.
[114]
G. Rallo et al., "Robust Experiment Design for Virtual Reference Feedback Tuning," i 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, s. 2271-2276.
[115]
T. Oomen och C. R. Rojas, "Sparse Iterative Learning Control (SPILC) : When to Sample for Resource-Efficiency?," i 2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), 2018, s. 497-502.
[116]
M. I. Müller et al., "A stochastic multi-armed bandit approach to nonparametric H∞-norm estimation," i 56th IEEE Conference on Decision and Control, 2017, s. 4632-4637.
[117]
G. Rallo et al., "Data-driven H-infinity-norm estimation via expert advice," i 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017, s. 1560-1565.
[118]
R. Mattila et al., "Identification of Hidden Markov Models Using Spectral Learning with Likelihood Maximization," i 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2017, s. 5859-5864.
[119]
R. Mattila et al., "Inverse filtering for hidden Markov models," i Advances in Neural Information Processing Systems, 2017, s. 4205-4214.
[120]
M. I. Müller, P. E. Valenzuela och C. R. Rojas, "Risk-coherent H2-optimal disturbance rejection under model uncertainty," i 20th IFAC World Congress, 2017, s. 15530-15535.
[121]
M. Galrinho, C. R. Rojas och H. Hjalmarsson, "A Weighted Least Squares Method for Estimation of Unstable Systems," i 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, s. 341-346.
[122]
N. Everitt et al., "Identification of modules in dynamic networks : An empirical Bayes approach," i 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, s. 4612-4617.
[123]
P. E. Valenzuela et al., "Particle-based Gaussian process optimization for input design in nonlinear dynamical models," i 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, s. 2085-2090.
[124]
K. Li et al., "Piecewise sparse signal recovery via piecewise orthogonal matching pursuit," i 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, s. 4608-4612.
[125]
M. Galrinho, C. Rojas och H. Hjalmarsson, "A Least Squares Method for Identification of Feedback Cascade Systems," i 17th IFAC Symposium on System Identification SYSID 2015 — Beijing, China, 19–21 October 2015, 2015, s. 98-103.
[126]
N. Blomberg, C. Rojas och B. Wahlberg, "Approximate Regularization Paths for Nuclear Norm Minimization using Singular Value Bounds : with Implementation and Extended Appendix," i Signal Processing Education Workshop, 2015.
[127]
D. Katselis, C. R. Rojas och C. L. Beck, "Estimator selection : End-performance metric aspects," i Proceedings of the American Control Conference, 2015, s. 4430-4435.
[128]
C. Rojas och B. Wahlberg, "How to monitor and mitigate stair-casing in L1 trend filtering," i ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, s. 3946-3950.
[129]
M. Galrinho, C. Rojas och H. Hjalmarsson, "On Estimating Initial Conditions in Unstructured Models," i 2015 54th IEEE Conference on Decision and Control (CDC), 2015, s. 2725-2730.
[130]
D. Katselis et al., "On experiment design for single carrier and multicarrier systems," i 2015 European Control Conference, ECC 2015, 2015, s. 1772-1777.
[131]
N. Everitt et al., "On the Variance Analysis of identified Linear MIMO Models," i IEEE Explore, 2015.
[132]
P. E. Valenzuela Pacheco, C. R. Rojas och H. Hjalmarsson, "Uncertainty in system identification : learning from the theory of risk," i IFAC-PapersOnLine, 2015, s. 1053-1058.
[133]
P. E. Valenzuela et al., "A graph/particle-based method for experiment design in nonlinear systems," i The 19th IFAC World Congress, 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24-29 August 2014, 2014, s. Paper MoB14.1.
[134]
B. I. Godoy et al., "A novel input design approach for systems with quantized output data," i 2014 European Control Conference, ECC, 2014, s. 1049-1054.
[135]
M. Galrinho, C. R. Rojas och H. Hjalmarsson, "A weighted least-squares method for parameter estimation in structured models," i Proceedings of the IEEE Conference on Decision and Control, 2014, s. 3322-3327.
[136]
A. Ebadat et al., "Application Set Approximation in Optimal Input Design for Model Predictive Control," i 2014 European Control Conference (ECC), 2014, s. 744-749.
[137]
A. Ebadat et al., "Applications Oriented Input Design for Closed-Loop System Identification : a Graph-Theory Approach," i 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, s. 4125-4130.
[138]
A. Ebadat et al., "Applications Oriented Input Design in Time-Domain Through Cyclic Methods," i 19th World Congress IFAC´14 World Congress; Cape Town, South Africa, 24-29 August 2014, 2014, s. 1422-1427.
[139]
N. Blomberg, C. R. Rojas och B. Wahlberg, "Approximate regularization path for nuclear norm based H2 model reduction," i Proceedings of the IEEE Conference on Decision and Control, 2014, s. 3637-3641.
[140]
K. Li et al., "Distributed Signal Recovery Via Piecewise Orthogonal Matching Pursuit and Finite-Time Consensus," i IEEE Explore, 2014.
[141]
D. Katselis, C. R. Rojas och H. Hjalmarsson, "Least Squares End Performance Experiment Design in Multicarrier Systems : The Sparse Preamble Case," i 2014 European Control Conference (ECC), 2014, s. 13-18.
[142]
H. Hjalmarsson och C. R. Rojas, "Model Structure Selection - An Update," i 2014 European Control Conference (ECC), 2014, s. 2382-2385.
[143]
K. Li et al., "Piecewise Toeplitz matrices-based sensing for rank minimization," i European Signal Processing Conference, 2014, s. 1836-1840.
[144]
M. Sundin et al., "Relevance Singular Vector Machine for low rank matrix sensing," i Signal Processing and Communications (SPCOM), 2014 International Conference on, 2014, s. 1-5.
[145]
N. Everitt, C. R. Rojas och H. Hjalmarsson, "Variance Results for Parallel Cascade Serial Systems," i Proceedings of 19th IFAC World Congress, 2014.
[146]
N. Everitt, C. R. Rojas och H. Hjalmarsson, "A Geometric Approach to Variance Analysis of Cascaded Systems," i Proceedings of the 52nd Conference On Decision And Control, 2013, s. 6496-6501.
[147]
C. R. Rojas, B. Wahlberg och H. Hjalmarsson, "A sparse estimation technique for general model structures," i 2013 European Control Conference, ECC 2013, 2013, s. 2410-2414.
[148]
D. Katselis, C. R. Rojas och H. Hjalmarsson, "Applications-oriented least squares experiment design in multicarrier communication systems," i Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2013), 2013, s. 74-79.
[149]
V. Krishnamurthy, C. R. Rojas och B. Wahlberg, "Computing monotone policies for Markov decision processes by exploiting sparsity," i 2013 3rd Australian Control Conference, AUCC 2013, 2013, s. 6697239.
[150]
T. Oomen et al., "Iteratively learning the H∞-norm of multivariable systems applied to model-error-modeling of a vibration isolation system," i Proceedings of the American Control Conference 2013, 2013, s. 6703-6708.
[151]
C. A. Larsson et al., "Model predictive control with integrated experiment design for output error systems," i 2013 European Control Conference, ECC 2013, 2013, s. 3790-3795.
[152]
P. E. Valenzuela Pacheco, C. R. Rojas och H. Hjalmarsson, "Optimal input design for non-linear dynamic systems : a graph theory approach," i 52nd IEEE Conference on Decision and Control, December 10-13, 2013 Florence, Italy, 2013, s. 5740-5745.
[153]
D. Katselis et al., "A Chernoff convexification for chance constrained MIMO training sequence design," i Signal Processing Advances in Wireless Communications (SPAWC), 2012 IEEE 13th International Workshop on, 2012, s. 40-44.
[154]
D. Katselis et al., "A Chernoff relaxation on the problem of application-oriented finite sample experiment design," i 2012 IEEE 51st Annual Conference on Decision and Control (CDC), 2012, s. 202-207.
[155]
K. E. J. Olofsson och C. R. Rojas, "A practical approach to input design for modal analysis using subspace methods," i 16th IFAC Symposium on System Identification, 2012, s. 362-367.
[156]
C. R. Rojas, J. Mårtensson och H. Hjalmarsson, "A tutorial on applications-oriented optimal experiment design," i Identification For Automotive Systems, 2012, s. 149-164.
[157]
D. Katselis et al., "Application-Oriented Finite Sample Experiment Design : A Semidefinite Relaxation Approach," i IFAC Proceedings Volumes (IFAC-PapersOnline) v16 nPART 1 : System Identification, 2012, s. 1635-1640.
[158]
H. Hjalmarsson, J. S. Welsh och C. R. Rojas, "Identification of box-jenkins models using structured ARX models and nuclear norm relaxation," i 16th IFAC Symposium on System Identification, 2012, s. 322-327.
[159]
D. Eckhard et al., "Mean-squared error experiment design for linear regression models," i 16th IFAC Symposium on System Identification, 2012, s. 1629-1634.
[160]
B. Wahlberg och C. R. Rojas, "On asymptotic frequency response variance expressions for estimated output error models," i 2012 IEEE 51st Annual Conference on Decision and Control (CDC), 2012, s. 178-183.
[161]
D. Eckhard et al., "On the convergence of the Prediction Error Method to its global minimum," i 16th IFAC Symposium on System Identification, 2012, s. 698-703.
[162]
R. Tóth, H. Hjalmarsson och C. R. Rojas, "Order and structural dependence selection of LPV-ARX models revisited," i Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 2012, s. 6271-6276.
[163]
D. Katselis et al., "Robust Experiment Design for System Identification via Semi-Infinite Programming Techniques," i Proceedings of the 16th IFAC Symposium on System Identification (SYSID 2012), 2012, s. 680-685.
[164]
R. Töth, H. Hjalmarsson och C. R. Rojas, "Sparse estimation or rational dynamical models," i 16th IFAC Symposium on System Identification, 2012, s. 983-988.
[165]
J. S. Welsh et al., "Sparse estimation techniques for basis function selection in wideband system identification," i 16th IFAC Symposium on System Identification, 2012, s. 977-982.
[166]
T. Oomen et al., "Analyzing Iterations in Identification with Application to Nonparametric H∞-Norm Estimation," i Proceedings of the 18th World Congress, The International Federation of Automatic Control, Milano (Italy) August 28 - September 2, 2011, 2011, s. 9972-9977.
[167]
K. E. J. Olofsson et al., "Cascade and multibatch subspace system identification for multivariate vacuum-plasma response characterisation," i Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC11), 2011, s. 2614-2619.
[168]
C. R. Rojas et al., "Chance Constrained Input Design," i Proceedings of the IEEE Conference on Decision and Control, 2011, s. 2957-2962.
[169]
C. R. Rojas, "Identifiability of Multivariable Dynamic Errors-In-Variables Systems," i Proceedings of the 9th IEEE International Conference on Control & Automation (IEEE ICCA’11), 2011, s. 189-194.
[170]
C. A. Larsson, C. R. Rojas och H. Hjalmarsson, "MPC Oriented Experiment Design," i Proceedings of the 18th IFAC World Congress, 2011, s. 9966-9971.
[171]
B. Wahlberg, M. Annergren och C. R. Rojas, "On Optimal Input Signal Design for Identification of Output Error Models," i Proceedings of the IEEE Conference on Decision and Control, 2011.
[172]
D. Katselis et al., "On Preamble-Based Channel Estimation in OFDM/OQAM Systems," i Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011), 2011, s. 1618-1622.
[173]
L. Huang, H. Hjalmarsson och C. R. Rojas, "On consistent estimation of farthest NMP zeros of stable LTI systems," i Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC11), 2011, s. 5094-5099.
[174]
B. Wahlberg, C. R. Rojas och M. Annergren, "On l1 Mean and Variance Filtering," i Proceedings of the 45th Annual Asilomar Conference on Signals, Systems, and Computers 2011, 2011, s. 1913-1916.
[175]
X. Bombois et al., "Optimal experiment design for hypothesis testing applied to functional magnetic resonance imaging," i Proceedings of the 18th IFAC World Congress, 2011, s. 9953-9958.
[176]
C. R. Rojas och H. Hjalmarsson, "Sparse estimation based on a validation criterion," i 2011 50th Conference On Decision And Control And European Control Conference, 2011, s. 2825-2830.
[177]
K. E. J. Olofsson et al., "Closed-loop MIMO ARX estimation of concurrent external plasma response eigenmodes in magnetic confinement fusion," i Proceedings of the 49th Conference on Decision and Control (CDC’10), 2010, s. 2954-2959.
[178]
C. A. Larsson, H. Hjalmarsson och C. R. Rojas, "Identification of nonlinear systems using misspecified predictors," i 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, s. 7214-7219.
[179]
C. A. Larsson, H. Hjalmarsson och C. R. Rojas, "On optimal input design for nonlinear FIR-type systems," i 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, s. 7220-7225.
[180]
J. S. Welsh och C. R. Rojas, "A Scenario Based Approach to Robust Experiment Design," i Proceedings of the 15th IFAC Symposium on System Identification (SYSID’09), 2009, s. 186-191.
[181]
C. R. Rojas et al., "Consistent estimation of real NMP zeros in stable LTI systems of arbitrary complexity," i 15th IFAC Symposium on System Identification, SYSID 2009, 2009, s. 922-927.
[182]
J. Mårtensson, C. R. Rojas och H. Hjalmarsson, "Finite Model Order Optimal Input Design for Minimum Variance Control," i European Control Conference, 2009, s. 454-459.
[183]
J. C. Agüero, C. R. Rojas och G. C. Goodwin, "Fundamental Limitations on the Accuracy of MIMO Linear Models Obtained by PEM for Systems Operating in Open Loop," i Proceedings of the Joint 48th IEEE Conference on Decision and Control (CDC’09) and 28th Chinese Control Conference (CCC’09), 2009, s. 482-487.
[184]
C. R. Rojas och H. Hjalmarsson, "Input design for asymptotic robust H2-filtering," i 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, 2009, s. 476-481.
[185]
C. Brighenti, B. Wahlberg och C. R. Rojas, "Input design using Markov chains for system identification," i Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, 2009, s. 1557-1562.
[186]
C. R. Rojas, H. Hjalmarsson och R. Hildebrand, "MIMO experiment design based on asymptotic model order theory," i Proceedings of the IEEE Conference on Decision and Control, 2009, s. 488-493.
[187]
K. E. J. Olofsson et al., "Vector dither experiment design and direct parametric identification of reversed-field pinch normal modes," i Proceedings of the IEEE Conference on Decision and Control, 2009, s. 1348-1353.
[188]
C. R. Rojas et al., "The cost of complexity in identification of FIR systems," i 17th World Congress, International Federation of Automatic Control, IFAC, 2008, s. 11451-11456.
[189]
C. R. Rojas, J. S. Welsh och G. C. Goodwin, "A Receding Horizon Algorithm to Generate Binary Signals with a Prescribed Autocovariance," i 2007 AMERICAN CONTROL CONFERENCE, VOLS 1-13, 2007, s. 122-127.
[190]
J. S. Welsh och C. R. Rojas, "Frequency Localising Basis Functions for Wide-band System Identification : A Condition Number Bound for Output Error Systems," i Proceedings of the European Control Conference (ECC), 2007, s. 4618-4624.
[191]
C. R. Rojas et al., "Optimal Experiment Design with Diffuse Prior Information," i Proceedings of the European Control Conference (ECC), 2007, s. 935-940.
[192]
J. S. Welsh, C. R. Rojas och S. D. Mitchell, "Wideband parametric identification of a power transformer," i 2007 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING, VOLS 1-2, 2007, s. 232-237.
[193]
R. A. Rojas och C. R. Rojas, "The inverse of sampling revisited," i Proceedings of the IASTED Conference on Intelligent Systems and Control (ISC-2001), 2001.
Kapitel i böcker
[194]
C. R. Rojas och M. I. Müller, "Algorithms for data-driven H∞-norm estimation," i Data-driven filter and control design : Methods and applications, Carlo Novara and Simone Formentin red., : IET Digital Library, 2019, s. 145-163.
[195]
C. R. Rojas et al., "Open-cut Mine Planning via Closed-loop Receding-horizon Optimal Control," i Identification and Control : The Gap between Theory and Practice, Ricardo S. Sánchez-Peña, Joseba Quevedo Casín and Vicenc Puig Cayuela red., London : Springer, 2007, s. 43-62.
Icke refereegranskade
Artiklar
[196]
H. Hjalmarsson, C. R. Rojas och D. E. Rivera, "System identification : A Wiener-Hammerstein benchmark," Control Engineering Practice, vol. 20, no. 11, s. 1095-1096, 2012.
Kapitel i böcker
[197]
C. R. Rojas och M. I. Müller, "Algorithms for data-driven H∞-norm estimation," i Data-Driven Modeling, Filtering and Control, : Institution of Engineering and Technology (IET), 2019, s. 145-163.
[198]
G. C. Goodwin, C. R. Rojas och J. S. Welsh, "Good, Bad and Optimal Experiments for Identification," i Forever Ljung in System Identification - Workshop on the occasion of Lennart Ljung’s 60th birthday, Torkel Glad and Gustaf Hendeby red., Lund, Sweden : Studentlitteratur, 2006.
Övriga
[199]
R. A. González et al., "Consistent identification of continuous-time systems under multisine input signal excitation," (Manuskript).
[200]
S. Pan et al., "Efficiency Analysis of the Simplified Refined Instrumental Variable Method for Continuous-time Systems," (Manuskript).
[201]
N. Everitt et al., "Variance Analysis of Linear SIMO Models with Spatially Correlated Noise," (Manuskript).
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2024-04-26 00:00:56