Publications by Cristian Rojas
Peer reviewed
Articles
[1]
R. A. González et al., "Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop," Automatica, vol. 166, 2024.
[2]
P. L. Wachel, K. Kowalczyk and C. R. Rojas, "Decentralized diffusion-based learning under non-parametric limited prior knowledge," European Journal of Control, vol. 75, 2024.
[3]
T. Oomen and C. R. Rojas, "Reset-free data-driven gain estimation : Power iteration using reversed-circulant matrices," Automatica, vol. 161, 2024.
[4]
J. Parsa, C. R. Rojas and H. Hjalmarsson, "Transformation of Regressors for Low Coherent Sparse System Identification," IEEE Transactions on Automatic Control, vol. 69, no. 5, pp. 2947-2962, 2024.
[5]
J. Parsa, C. R. Rojas and H. Hjalmarsson, "Application-Oriented Input Design With Low Coherence Constraint," IEEE Control Systems Letters, vol. 7, pp. 193-198, 2023.
[6]
J. Parsa, C. R. Rojas and H. Hjalmarsson, "Coherence-Based Input Design for Nonlinear Systems," IEEE Control Systems Letters, vol. 7, pp. 2934-2939, 2023.
[7]
R. A. González et al., "On the Relation Between Discrete and Continuous-Time Refined Instrumental Variable Methods," IEEE Control Systems Letters, vol. 7, pp. 2233-2238, 2023.
[8]
S. Pan et al., "Consistency analysis and bias elimination of the Instrumental-Variable-based State Variable Filter method," Automatica, vol. 144, 2022.
[9]
C. R. Rojas and P. Wachel, "On State-Space Representations of General Discrete-Time Dynamical Systems," IEEE Transactions on Automatic Control, vol. 67, no. 12, pp. 6975-6979, 2022.
[10]
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, pp. 119-129, 2022.
[11]
R. A. González et al., "Refined instrumental variable methods for unstable continuous-time systems in closed-loop," International Journal of Control, 2022.
[12]
M. I. Müller and C. R. Rojas, "Risk-theoretic optimal design of output-feedback controllers via iterative convex relaxations," Automatica, vol. 136, pp. 110042-110042, 2022.
[13]
R. A. González et al., "Theoretical and practical aspects of the convergence of the SRIVC estimator for over-parameterized models," Automatica, vol. 142, pp. 110355-110355, 2022.
[14]
R. A. González et al., "Consistent identification of continuous-time systems under multisine input signal excitation," Automatica, vol. 133, 2021.
[15]
B. Djehiche, O. Mazhar and C. R. Rojas, "Finite impulse response models : A non-asymptotic analysis of the least squares estimator," Bernoulli, vol. 27, no. 2, pp. 976-1000, 2021.
[16]
I. Lourenço et al., "Hidden Markov Models : Inverse Filtering, Belief Estimation and Privacy Protection," Journal of Systems Science and Complexity, vol. 34, no. 5, pp. 1801-1820, 2021.
[17]
S. Pan et al., "Consistency analysis of the Simplified Refined Instrumental Variable method for Continuous-time systems," Automatica, vol. 113, 2020.
[18]
S. Pan et al., "Efficiency analysis of the Simplified Refined Instrumental Variable method for Continuous-time systems," Automatica, vol. 121, 2020.
[19]
R. Mattila et al., "Inverse Filtering for Hidden Markov Models With Applications to Counter-Adversarial Autonomous Systems," IEEE Transactions on Signal Processing, vol. 68, pp. 4987-5002, 2020.
[20]
R. Mattila et al., "Estimating Private Beliefs of Bayesian Agents Based on Observed Decisions," IEEE Control Systems Letters, pp. 523-528, 2019.
[21]
M. Galrinho, C. R. Rojas and H. Hjalmarsson, "Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting," Automatica, vol. 102, pp. 45-57, 2019.
[22]
P. E. Valenzuela, T. B. Schön and C. R. Rojas, "On model order priors for Bayesian identification of SISO linear systems," International Journal of Control, vol. 92, no. 7, pp. 1645-1661, 2019.
[23]
M. Galrinho, C. R. Rojas and H. Hjalmarsson, "Parametric Identification Using Weighted Null-Space Fitting," IEEE Transactions on Automatic Control, vol. 64, no. 7, pp. 2798-2813, 2019.
[24]
H. Kwon et al., "Three-harmonic optimal multisine input power spectrum for bioimpedance identification," Physiological Measurement, vol. 40, no. 5, 2019.
[25]
M. Sadeghi, C. R. Rojas and B. Wahlberg, "A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization," IFAC-PapersOnLine, vol. 51, no. 15, pp. 96-101, 2018.
[26]
R. A. González and C. R. Rojas, "A fully Bayesian approach to kernel-based regularization for impulse response estimation⁎," IFAC-PapersOnLine, vol. 51, no. 15, pp. 186-191, 2018.
[27]
H. Ha et al., "An analysis of the SPARSEVA estimate for the finite sample data case," Automatica, vol. 96, pp. 141-149, 2018.
[28]
P. E. Valenzuela, C. R. Rojas and H. Hjalmarsson, "Analysis of averages over distributions of Markov processes," Automatica, vol. 98, pp. 354-357, 2018.
[29]
M. . R. Abdalmoaty, C. R. Rojas and H. Hjalmarsson, "Identification of a Class of Nonlinear Dynamical Networks⁎," IFAC-PapersOnLine, vol. 51, no. 15, pp. 868-873, 2018.
[30]
J. Bjurgert, P. E. Valenzuela and C. R. Rojas, "On Adaptive Boosting for System Identification," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4510-4514, 2018.
[31]
R. Mattila et al., "Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models," IEEE Signal Processing Letters, vol. 24, no. 12, pp. 1813-1817, 2017.
[32]
R. Mattila et al., "Computing monotone policies for Markov decision processes : a nearly-isotonic penalty approach," IFAC-PapersOnLine, vol. 50, no. 1, pp. 8429-8434, 2017.
[33]
D. Eckhard et al., "Cost function shaping of the output error criterion," Automatica, vol. 76, pp. 53-60, 2017.
[34]
A. Ebadat et al., "Model Predictive Control oriented experiment design for system identification : A graph theoretical approach," Journal of Process Control, vol. 52, pp. 75-84, 2017.
[35]
P. E. Valenzuela et al., "On robust input design for nonlinear dynamical models," Automatica, vol. 77, pp. 268-278, 2017.
[36]
O. Trollberg, C. Rojas and E. W. Jacobsen, "Online constraint adaptation in economic model predictive control," IFAC-PapersOnLine, vol. 50, no. 1, pp. 9065-9070, 2017.
[37]
R. A. González et al., "Optimal enforcement of causality in non-parametric transfer function estimation," IEEE Control Systems Letters, vol. 1, no. 2, pp. 268-273, 2017.
[38]
T. Oomen and C. R. Rojas, "Sparse iterative learning control with application to a wafer stage : Achieving performance, resource efficiency, and task flexibility," Mechatronics (Oxford), vol. 47, pp. 134-147, 2017.
[39]
N. Everitt et al., "Variance analysis of linear SIMO models with spatially correlated noise," Automatica, vol. 77, pp. 68-81, 2017.
[40]
M. Malek Mohammadi, C. Rojas and B. Wahlberg, "A Class of Nonconvex Penalties Preserving OverallConvexity in Optimization-Based Mean Filtering," IEEE Transactions on Signal Processing, vol. 65, no. 24, pp. 6650-6664, 2016.
[41]
J. Ottersten, B. Wahlberg and C. R. Rojas, "Accurate Changing Point Detection for l(1) Mean Filtering," IEEE Signal Processing Letters, vol. 23, no. 2, pp. 297-301, 2016.
[42]
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, pp. 126-142, 2016.
[43]
K. Li et al., "Alternating strategies with internal ADMM for low-rank matrix reconstruction," Signal Processing, vol. 121, pp. 153-159, 2016.
[44]
C. A. Larsson et al., "An application-oriented approach to dual control with excitation for closed-loop identification," European Journal of Control, vol. 29, pp. 1-16, 2016.
[45]
M. Sundin et al., "Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction," IEEE Transactions on Signal Processing, vol. 64, no. 20, pp. 5327-5339, 2016.
[46]
M. Malek-Mohammadi et al., "Successive Concave Sparsity Approximation for Compressed Sensing," IEEE Transactions on Signal Processing, vol. 64, no. 21, pp. 5657-5671, 2016.
[47]
M. Malek Mohammadi et al., "Upper bounds on the error of sparse vector and low-rank matrix recovery," Signal Processing, vol. 120, pp. 249-254, 2016.
[48]
C. R. Rojas, P. E. Valenzuela and R. A. Rojas, "A Critical View on Benchmarks based on Randomly Generated Systems," IFAC-PapersOnLine, vol. 48, no. 28, pp. 1471-1476, 2015.
[49]
P. E. Valenzuela, C. R. Rojas and H. Hjalmarsson, "A graph theoretical approach to input design for identification of nonlinear dynamical models," Automatica, vol. 51, pp. 233-242, 2015.
[50]
D. Katselis and C. R. Rojas, "Application-Oriented Estimator Selection," IEEE Signal Processing Letters, vol. 22, no. 4, pp. 489-493, 2015.
[51]
R. Mattila, C. R. Rojas and B. Wahlberg, "Evaluation of Spectral Learning for the Identification of Hidden Markov Models," IFAC-PapersOnLine, vol. 48, no. 28, pp. 897-902, 2015.
[52]
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, pp. 1-16, 2015.
[53]
N. Everitt et al., "On the Effect of Noise Correlation in Parameter Identification of SIMO Systems," IFAC-PapersOnLine, vol. 48, no. 28, pp. 326-331, 2015.
[54]
D. Katselis et al., "On the end-performance metric estimator selection," Automatica, vol. 58, pp. 22-27, 2015.
[55]
N. Blomberg, C. R. Rojas and B. Wahlberg, "Regularization Paths for Re-Weighted Nuclear Norm Minimization," IEEE Signal Processing Letters, vol. 22, no. 11, pp. 1980-1984, 2015.
[56]
H. Ha et al., "Reweighted nuclear norm regularization : A SPARSEVA approach," IFAC-PapersOnLine, vol. 48, no. 28, pp. 1172-1177, 2015.
[57]
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, pp. 2247-2260, 2014.
[58]
V. Krishnamurthy and C. R. Rojas, "Reduced Complexity HMM Filtering With Stochastic Dominance Bounds : A Convex Optimization Approach," IEEE Transactions on Signal Processing, vol. 62, no. 23, pp. 6309-6322, 2014.
[59]
B. Sanchez and C. R. Rojas, "Robust excitation power spectrum design for broadband impedance spectroscopy," Measurement science and technology, vol. 25, no. 6, pp. 065501, 2014.
[60]
C. R. Rojas, R. Toth and H. Hjalmarsson, "Sparse Estimation of Polynomial and Rational Dynamical Models," IEEE Transactions on Automatic Control, vol. 59, no. 11, pp. 2962-2977, 2014.
[61]
C. R. Rojas, D. Katselis and H. Hjalmarsson, "A Note on the SPICE Method," IEEE Transactions on Signal Processing, vol. 61, no. 18, pp. 4545-4551, 2013.
[62]
D. Katselis et al., "Frequency smoothing gains in preamble-based channel estimation for multicarrier systems," Signal Processing, vol. 93, no. 9, pp. 2777-2782, 2013.
[63]
D. Eckhard et al., "Input design as a tool to improve the convergence of PEM," Automatica, vol. 49, no. 11, pp. 3282-3291, 2013.
[64]
D. Katselis et al., "Training sequence design for MIMO channels: an application-oriented approach," EURASIP Journal on Wireless Communications and Networking, vol. 2013, pp. 245, 2013.
[65]
J. C. Agüero et al., "Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation," Automatica, vol. 48, no. 4, pp. 632-637, 2012.
[66]
C. R. Rojas et al., "Analyzing iterations in identification with application to nonparametric H-infinity-norm estimation," Automatica, vol. 48, no. 11, pp. 2776-2790, 2012.
[67]
C. Mueller, C. R. Rojas and G. C. Goodwin, "Generation of amplitude constrained signals with a prescribed spectrum," Automatica, vol. 48, no. 1, pp. 153-158, 2012.
[68]
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, pp. 085702, 2012.
[69]
C. R. Rojas et al., "Robustness in Experiment Design," IEEE Transactions on Automatic Control, vol. 57, no. 4, pp. 860-874, 2012.
[70]
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, pp. 1388-1398, 2011.
[71]
A. Esparza et al., "Asymptotic statistical analysis for model-based control design strategies," Automatica, vol. 47, no. 5, pp. 1041-1046, 2011.
[72]
J. Mårtensson, C. R. Rojas and H. Hjalmarsson, "Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller," Automatica, vol. 47, no. 3, pp. 578-583, 2011.
[73]
H. Hjalmarsson et al., "On the accuracy in errors-in-variables identification compared to prediction-error identification," Automatica, vol. 47, no. 12, pp. 2704-2712, 2011.
[74]
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, pp. 084003, 2011.
[75]
C. R. Rojas et al., "The cost of complexity in system identification: The Output Error case," Automatica, vol. 47, no. 9, pp. 1938-1948, 2011.
[76]
C. R. Rojas, P. Zetterberg and P. Händel, "Transceiver Inphase/Quadrature Imbalance, Ellipse Fitting, and the Universal Software Radio Peripheral," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 11, pp. 3629-3639, 2011.
[77]
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, pp. 83-94, 2010.
[78]
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, pp. 2298-2309, 2010.
[79]
C. R. Rojas, J. S. Welsh and J. C. Agüero, "Fundamental Limitations on the Variance of Estimated Parametric Models," IEEE Transactions on Automatic Control, vol. 54, no. 5, pp. 1077-1081, 2009.
[80]
C. R. Rojas et al., "On the Equivalence of Least Costly and Traditional Experiment Design for Control," Automatica, vol. 44, no. 11, pp. 2706-2715, 2008.
[81]
G. C. Goodwin et al., "Robust Identification of Process Models from Plant Data," Journal of Process Control, vol. 18, no. 9, pp. 810-820, 2008.
[82]
C. R. Rojas et al., "Robust Optimal Experiment Design for System Identification," Automatica, vol. 43, no. 6, pp. 993-1008, 2007.
[83]
C. R. Rojas, R. A. Rojas and M. E. Salgado, "Equivalence between Transfer-Matrix and Observed-State Feedback Control," IEE Proceedings - Control Theory and Applications, vol. 153, no. 2, pp. 147-155, 2006.
Conference papers
[84]
J. He, C. R. Rojas and H. Hjalmarsson, "A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories," in IFAC-PapersOnLine, 2024, pp. 169-174.
[85]
K. Colin et al., "A bias-variance perspective of data-driven control," in IFAC-Papers OnLine, 2024, pp. 85-90.
[86]
D. Materassi et al., "Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I)," in IFAC-PapersOnLine, 2024, pp. 492-496.
[87]
D. Materassi et al., "Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II)," in IFAC-PapersOnLine, 2024, pp. 497-501.
[88]
F. Dettù et al., "From Data to Control : A Two-Stage Simulation-Based Approach," in 2024 European Control Conference, ECC 2024, 2024, pp. 3428-3433.
[89]
K. Kowalczyk, P. Wachel and C. R. Rojas, "Kernel-Based Learning with Guarantees for Multi-agent Applications," in Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings, 2024, pp. 479-487.
[90]
J. He, C. R. Rojas and H. Hjalmarsson, "Weighted Least-Squares PARSIM," in IFAC-PapersOnLine, 2024, pp. 330-335.
[91]
B. Lakshminarayanan and C. R. Rojas, "A Unified Approach to Differentially Private Bayes Point Estimation," in 22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023, 2023, pp. 8375-8380.
[92]
R. A. González et al., "An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification," in IFAC-PapersOnLine, 2023, pp. 4204-4209.
[93]
A. Elton et al., "Blind Nonparametric Estimation of SISO Continuous-time Systems," in IFAC-PapersOnLine, 2023, pp. 4222-4227.
[94]
F. Quinzan et al., "DRCFS : Doubly Robust Causal Feature Selection," in Proceedings of the 40th International Conference on Machine Learning, ICML 2023, 2023, pp. 28468-28491.
[95]
I. Lourenço et al., "Diagnosing and Repairing Feature Representations Under Distribution Shifts," in 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, pp. 3638-3645.
[96]
B. Lakshminarayanan and C. R. Rojas, "Minimax Two-Stage Gradient Boosting for Parameter Estimation," in 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, pp. 1189-1194.
[97]
R. Winqvist et al., "Optimal Transport for Correctional Learning," in 2023 62nd IEEE Conference on Decision and Control, CDC 2023, 2023, pp. 6806-6812.
[98]
A. Elton et al., "Parametric Continuous-Time Blind System Identification," in 2023 62nd IEEE Conference on Decision and Control, CDC2023, 2023, pp. 1474-1479.
[99]
R. A. González et al., "Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach," in 22nd IFAC World CongressYokohama, Japan, July 9-14, 2023, 2023, pp. 4216-4221.
[100]
B. Lakshminarayanan and C. R. Rojas, "A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation," in 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, pp. 5369-5374.
[101]
I. Lourenço et al., "A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning," in 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, pp. 3456-3461.
[102]
J. Parsa, C. R. Rojas and H. Hjalmarsson, "Optimal Input Design for Sparse System Identification," in 2022 EUROPEAN CONTROL CONFERENCE (ECC), 2022, pp. 1999-2004.
[103]
I. Lourenço et al., "Cooperative System Identification via Correctional Learning," in IFAC PAPERSONLINE, 2021, pp. 19-24.
[104]
M. I. Müller et al., "Data-Driven Input-Passivity Estimation Using Power Iterations," in IFAC PAPERSONLINE, 2021, pp. 619-624.
[105]
R. A. González, C. R. Rojas and H. Hjalmarsson, "Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations," in Proceedings 2021 60th IEEE conference on decision and control (CDC), 2021, pp. 114-119.
[106]
R. A. González et al., "The SRIVC algorithm for continuous-time system identification with arbitrary input excitation in open and closed loop," in Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021.
[107]
R. A. González and C. R. Rojas, "A Finite-Sample Deviation Bound for Stable Autoregressive Processes," in A Finite-Sample Deviation Bound for Stable Autoregressive Processes, 2020, pp. 1-10.
[108]
R. A. González, W. James S. and C. R. Rojas, "Enforcing stability through ellipsoidal inner approximations in the indirect approach for continuous-time system identification," in 21st IFAC World Congress (IFAC 2020), Berlin, Germany, 2020, pp. 566-571.
[109]
R. Mattila et al., "Fast and consistent learning of hidden markov models by incorporating non-consecutive correlations," in 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 6741-6752.
[110]
R. A. González and C. R. Rojas, "Finite sample deviation and variance bounds for first order autoregressive processes," in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020, pp. 5380-5384.
[111]
I. Lourenço et al., "How to Protect Your Privacy? : A Framework for Counter-Adversarial Decision Making," in Proceedings of the IEEE Conference on Decision and Control, 2020, pp. 1785-1791.
[112]
M. I. Müller and C. R. Rojas, "Iterative H-infinity-norm Estimation Using Cyclic-Prefixed Signals," in 2020 59Th IEEE Conference On Decision And Control (Cdc), 2020, pp. 2869-2874.
[113]
M. Müller and C. R. Rojas, "Iterative H∞-norm estimation using cyclic-prefixed signals," in 59th IEEE Conference on Decision and Control, 2020.
[114]
R. Mattila et al., "What did your adversary believe? : Optimal filtering and smoothing in counter-adversarial autonomous systems," in 2020 IEEE international conference on acoustics, speech, and signal processing, 2020, pp. 5495-5499.
[115]
M. I. Müller and C. R. Rojas, "Gain estimation of linear dynamical systems using Thompson Sampling," in Proceedings of Machine Learning Research, 2019, pp. 1535-1543.
[116]
M. I. Müller and C. R. Rojas, "Risk-Coherent H∞-optimal Filter Design Under Model Uncertainty with Applications to MISO Control," in 2019 18th European Control Conference, ECC 2019, 2019, pp. 1461-1466.
[117]
M. I. Müller and C. R. Rojas, "A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems," in IFAC-PapersOnLine, 2018, pp. 84-89.
[118]
M. I. Müller et al., "A Risk-Theoretical Approach to H2-Optimal Control under Covert Attacks," in 57th IEEE Conference on Decision and Control, 2018, pp. 4553-4558.
[119]
R. A. Gonzalez, C. R. Rojas and J. S. Welsh, "An asymptotically optimal indirect approach to continuous-time system identification," in 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, pp. 638-643.
[120]
O. Mazhar et al., "Bayesian model selection for change point detection and clustering," in 35th International Conference on Machine Learning, ICML 2018, 2018, pp. 5497-5520.
[121]
M. Abdalmoaty, C. R. Rojas and H. Hjalmarsson, "Identication of a Class of Nonlinear Dynamical Networks," in 18th IFAC Symposium on System Identification, 2018.
[122]
R. Mattila et al., "Inverse Filtering for Linear Gaussian State-Space Models," in Proceedings of the 57th IEEE Conference on Decision and Control (CDC’18), Miami Beach, FL, USA, 2018., 2018.
[123]
O. Trollberg et al., "On optimization of paper machines using economic model predictive control," in Paper Conference and Trade Show, PaperCon 2018, 2018, pp. 286-293.
[124]
G. Rallo et al., "Robust Experiment Design for Virtual Reference Feedback Tuning," in 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, pp. 2271-2276.
[125]
T. Oomen and C. R. Rojas, "Sparse Iterative Learning Control (SPILC) : When to Sample for Resource-Efficiency?," in 2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), 2018, pp. 497-502.
[126]
M. I. Müller et al., "A stochastic multi-armed bandit approach to nonparametric H∞-norm estimation," in 56th IEEE Conference on Decision and Control, 2017, pp. 4632-4637.
[127]
G. Rallo et al., "Data-driven H-infinity-norm estimation via expert advice," in 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017, pp. 1560-1565.
[128]
R. Mattila et al., "Identification of Hidden Markov Models Using Spectral Learning with Likelihood Maximization," in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2017, pp. 5859-5864.
[129]
R. Mattila et al., "Inverse filtering for hidden Markov models," in Advances in Neural Information Processing Systems, 2017, pp. 4205-4214.
[130]
M. I. Müller, P. E. Valenzuela and C. R. Rojas, "Risk-coherent H2-optimal disturbance rejection under model uncertainty," in 20th IFAC World Congress, 2017, pp. 15530-15535.
[131]
M. Galrinho, C. R. Rojas and H. Hjalmarsson, "A Weighted Least Squares Method for Estimation of Unstable Systems," in 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, pp. 341-346.
[132]
N. Everitt et al., "Identification of modules in dynamic networks : An empirical Bayes approach," in 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, pp. 4612-4617.
[133]
P. E. Valenzuela et al., "Particle-based Gaussian process optimization for input design in nonlinear dynamical models," in 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 2016, pp. 2085-2090.
[134]
K. Li et al., "Piecewise sparse signal recovery via piecewise orthogonal matching pursuit," in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4608-4612.
[135]
M. Galrinho, C. Rojas and H. Hjalmarsson, "A Least Squares Method for Identification of Feedback Cascade Systems," in 17th IFAC Symposium on System Identification SYSID 2015 — Beijing, China, 19–21 October 2015, 2015, pp. 98-103.
[136]
N. Blomberg, C. Rojas and B. Wahlberg, "Approximate Regularization Paths for Nuclear Norm Minimization using Singular Value Bounds : with Implementation and Extended Appendix," in Signal Processing Education Workshop, 2015.
[137]
D. Katselis, C. R. Rojas and C. L. Beck, "Estimator selection : End-performance metric aspects," in Proceedings of the American Control Conference, 2015, pp. 4430-4435.
[138]
C. Rojas and B. Wahlberg, "How to monitor and mitigate stair-casing in L1 trend filtering," in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, pp. 3946-3950.
[139]
M. Galrinho, C. Rojas and H. Hjalmarsson, "On Estimating Initial Conditions in Unstructured Models," in 2015 54th IEEE Conference on Decision and Control (CDC), 2015, pp. 2725-2730.
[140]
D. Katselis et al., "On experiment design for single carrier and multicarrier systems," in 2015 European Control Conference, ECC 2015, 2015, pp. 1772-1777.
[141]
N. Everitt et al., "On the Variance Analysis of identified Linear MIMO Models," in IEEE Explore, 2015.
[142]
P. E. Valenzuela Pacheco, C. R. Rojas and H. Hjalmarsson, "Uncertainty in system identification : learning from the theory of risk," in IFAC-PapersOnLine, 2015, pp. 1053-1058.
[143]
P. E. Valenzuela et al., "A graph/particle-based method for experiment design in nonlinear systems," in The 19th IFAC World Congress, 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24-29 August 2014, 2014, p. Paper MoB14.1.
[144]
B. I. Godoy et al., "A novel input design approach for systems with quantized output data," in 2014 European Control Conference, ECC, 2014, pp. 1049-1054.
[145]
M. Galrinho, C. R. Rojas and H. Hjalmarsson, "A weighted least-squares method for parameter estimation in structured models," in Proceedings of the IEEE Conference on Decision and Control, 2014, pp. 3322-3327.
[146]
A. Ebadat et al., "Application Set Approximation in Optimal Input Design for Model Predictive Control," in 2014 European Control Conference (ECC), 2014, pp. 744-749.
[147]
A. Ebadat et al., "Applications Oriented Input Design for Closed-Loop System Identification : a Graph-Theory Approach," in 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, pp. 4125-4130.
[148]
A. Ebadat et al., "Applications Oriented Input Design in Time-Domain Through Cyclic Methods," in 19th World Congress IFAC´14 World Congress; Cape Town, South Africa, 24-29 August 2014, 2014, pp. 1422-1427.
[149]
N. Blomberg, C. R. Rojas and B. Wahlberg, "Approximate regularization path for nuclear norm based H2 model reduction," in Proceedings of the IEEE Conference on Decision and Control, 2014, pp. 3637-3641.
[150]
K. Li et al., "Distributed Signal Recovery Via Piecewise Orthogonal Matching Pursuit and Finite-Time Consensus," in IEEE Explore, 2014.
[151]
D. Katselis, C. R. Rojas and H. Hjalmarsson, "Least Squares End Performance Experiment Design in Multicarrier Systems : The Sparse Preamble Case," in 2014 European Control Conference (ECC), 2014, pp. 13-18.
[152]
H. Hjalmarsson and C. R. Rojas, "Model Structure Selection - An Update," in 2014 European Control Conference (ECC), 2014, pp. 2382-2385.
[153]
K. Li et al., "Piecewise Toeplitz matrices-based sensing for rank minimization," in European Signal Processing Conference, 2014, pp. 1836-1840.
[154]
M. Sundin et al., "Relevance Singular Vector Machine for low rank matrix sensing," in Signal Processing and Communications (SPCOM), 2014 International Conference on, 2014, pp. 1-5.
[155]
N. Everitt, C. R. Rojas and H. Hjalmarsson, "Variance Results for Parallel Cascade Serial Systems," in Proceedings of 19th IFAC World Congress, 2014.
[156]
N. Everitt, C. R. Rojas and H. Hjalmarsson, "A Geometric Approach to Variance Analysis of Cascaded Systems," in Proceedings of the 52nd Conference On Decision And Control, 2013, pp. 6496-6501.
[157]
C. R. Rojas, B. Wahlberg and H. Hjalmarsson, "A sparse estimation technique for general model structures," in 2013 European Control Conference, ECC 2013, 2013, pp. 2410-2414.
[158]
D. Katselis, C. R. Rojas and H. Hjalmarsson, "Applications-oriented least squares experiment design in multicarrier communication systems," in Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2013), 2013, pp. 74-79.
[159]
V. Krishnamurthy, C. R. Rojas and B. Wahlberg, "Computing monotone policies for Markov decision processes by exploiting sparsity," in 2013 3rd Australian Control Conference, AUCC 2013, 2013, p. 6697239.
[160]
T. Oomen et al., "Iteratively learning the H∞-norm of multivariable systems applied to model-error-modeling of a vibration isolation system," in Proceedings of the American Control Conference 2013, 2013, pp. 6703-6708.
[161]
C. A. Larsson et al., "Model predictive control with integrated experiment design for output error systems," in 2013 European Control Conference, ECC 2013, 2013, pp. 3790-3795.
[162]
P. E. Valenzuela Pacheco, C. R. Rojas and H. Hjalmarsson, "Optimal input design for non-linear dynamic systems : a graph theory approach," in 52nd IEEE Conference on Decision and Control, December 10-13, 2013 Florence, Italy, 2013, pp. 5740-5745.
[163]
D. Katselis et al., "A Chernoff convexification for chance constrained MIMO training sequence design," in Signal Processing Advances in Wireless Communications (SPAWC), 2012 IEEE 13th International Workshop on, 2012, pp. 40-44.
[164]
D. Katselis et al., "A Chernoff relaxation on the problem of application-oriented finite sample experiment design," in 2012 IEEE 51st Annual Conference on Decision and Control (CDC), 2012, pp. 202-207.
[165]
K. E. J. Olofsson and C. R. Rojas, "A practical approach to input design for modal analysis using subspace methods," in 16th IFAC Symposium on System Identification, 2012, pp. 362-367.
[166]
C. R. Rojas, J. Mårtensson and H. Hjalmarsson, "A tutorial on applications-oriented optimal experiment design," in Identification For Automotive Systems, 2012, pp. 149-164.
[167]
D. Katselis et al., "Application-Oriented Finite Sample Experiment Design : A Semidefinite Relaxation Approach," in IFAC Proceedings Volumes (IFAC-PapersOnline) v16 nPART 1 : System Identification, 2012, pp. 1635-1640.
[168]
H. Hjalmarsson, J. S. Welsh and C. R. Rojas, "Identification of box-jenkins models using structured ARX models and nuclear norm relaxation," in 16th IFAC Symposium on System Identification, 2012, pp. 322-327.
[169]
D. Eckhard et al., "Mean-squared error experiment design for linear regression models," in 16th IFAC Symposium on System Identification, 2012, pp. 1629-1634.
[170]
B. Wahlberg and C. R. Rojas, "On asymptotic frequency response variance expressions for estimated output error models," in 2012 IEEE 51st Annual Conference on Decision and Control (CDC), 2012, pp. 178-183.
[171]
D. Eckhard et al., "On the convergence of the Prediction Error Method to its global minimum," in 16th IFAC Symposium on System Identification, 2012, pp. 698-703.
[172]
R. Tóth, H. Hjalmarsson and C. R. Rojas, "Order and structural dependence selection of LPV-ARX models revisited," in Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 2012, pp. 6271-6276.
[173]
D. Katselis et al., "Robust Experiment Design for System Identification via Semi-Infinite Programming Techniques," in Proceedings of the 16th IFAC Symposium on System Identification (SYSID 2012), 2012, pp. 680-685.
[174]
R. Töth, H. Hjalmarsson and C. R. Rojas, "Sparse estimation or rational dynamical models," in 16th IFAC Symposium on System Identification, 2012, pp. 983-988.
[175]
J. S. Welsh et al., "Sparse estimation techniques for basis function selection in wideband system identification," in 16th IFAC Symposium on System Identification, 2012, pp. 977-982.
[176]
T. Oomen et al., "Analyzing Iterations in Identification with Application to Nonparametric H∞-Norm Estimation," in Proceedings of the 18th World Congress, The International Federation of Automatic Control, Milano (Italy) August 28 - September 2, 2011, 2011, pp. 9972-9977.
[177]
K. E. J. Olofsson et al., "Cascade and multibatch subspace system identification for multivariate vacuum-plasma response characterisation," in Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC11), 2011, pp. 2614-2619.
[178]
C. R. Rojas et al., "Chance Constrained Input Design," in Proceedings of the IEEE Conference on Decision and Control, 2011, pp. 2957-2962.
[179]
C. R. Rojas, "Identifiability of Multivariable Dynamic Errors-In-Variables Systems," in Proceedings of the 9th IEEE International Conference on Control & Automation (IEEE ICCA’11), 2011, pp. 189-194.
[180]
C. A. Larsson, C. R. Rojas and H. Hjalmarsson, "MPC Oriented Experiment Design," in Proceedings of the 18th IFAC World Congress, 2011, pp. 9966-9971.
[181]
B. Wahlberg, M. Annergren and C. R. Rojas, "On Optimal Input Signal Design for Identification of Output Error Models," in Proceedings of the IEEE Conference on Decision and Control, 2011.
[182]
D. Katselis et al., "On Preamble-Based Channel Estimation in OFDM/OQAM Systems," in Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011), 2011, pp. 1618-1622.
[183]
L. Huang, H. Hjalmarsson and C. R. Rojas, "On consistent estimation of farthest NMP zeros of stable LTI systems," in Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC11), 2011, pp. 5094-5099.
[184]
B. Wahlberg, C. R. Rojas and M. Annergren, "On l1 Mean and Variance Filtering," in Proceedings of the 45th Annual Asilomar Conference on Signals, Systems, and Computers 2011, 2011, pp. 1913-1916.
[185]
X. Bombois et al., "Optimal experiment design for hypothesis testing applied to functional magnetic resonance imaging," in Proceedings of the 18th IFAC World Congress, 2011, pp. 9953-9958.
[186]
C. R. Rojas and H. Hjalmarsson, "Sparse estimation based on a validation criterion," in 2011 50th Conference On Decision And Control And European Control Conference, 2011, pp. 2825-2830.
[187]
K. E. J. Olofsson et al., "Closed-loop MIMO ARX estimation of concurrent external plasma response eigenmodes in magnetic confinement fusion," in Proceedings of the 49th Conference on Decision and Control (CDC’10), 2010, pp. 2954-2959.
[188]
C. A. Larsson, H. Hjalmarsson and C. R. Rojas, "Identification of nonlinear systems using misspecified predictors," in 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, pp. 7214-7219.
[189]
C. A. Larsson, H. Hjalmarsson and C. R. Rojas, "On optimal input design for nonlinear FIR-type systems," in 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, pp. 7220-7225.
[190]
J. S. Welsh and C. R. Rojas, "A Scenario Based Approach to Robust Experiment Design," in Proceedings of the 15th IFAC Symposium on System Identification (SYSID’09), 2009, pp. 186-191.
[191]
C. R. Rojas et al., "Consistent estimation of real NMP zeros in stable LTI systems of arbitrary complexity," in 15th IFAC Symposium on System Identification, SYSID 2009, 2009, pp. 922-927.
[192]
J. Mårtensson, C. R. Rojas and H. Hjalmarsson, "Finite Model Order Optimal Input Design for Minimum Variance Control," in European Control Conference, 2009, pp. 454-459.
[193]
J. C. Agüero, C. R. Rojas and G. C. Goodwin, "Fundamental Limitations on the Accuracy of MIMO Linear Models Obtained by PEM for Systems Operating in Open Loop," in Proceedings of the Joint 48th IEEE Conference on Decision and Control (CDC’09) and 28th Chinese Control Conference (CCC’09), 2009, pp. 482-487.
[194]
C. R. Rojas and H. Hjalmarsson, "Input design for asymptotic robust H2-filtering," in 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, 2009, pp. 476-481.
[195]
C. Brighenti, B. Wahlberg and C. R. Rojas, "Input design using Markov chains for system identification," in Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, 2009, pp. 1557-1562.
[196]
C. R. Rojas, H. Hjalmarsson and R. Hildebrand, "MIMO experiment design based on asymptotic model order theory," in Proceedings of the IEEE Conference on Decision and Control, 2009, pp. 488-493.
[197]
K. E. J. Olofsson et al., "Vector dither experiment design and direct parametric identification of reversed-field pinch normal modes," in Proceedings of the IEEE Conference on Decision and Control, 2009, pp. 1348-1353.
[198]
C. R. Rojas et al., "The cost of complexity in identification of FIR systems," in 17th World Congress, International Federation of Automatic Control, IFAC, 2008, pp. 11451-11456.
[199]
C. R. Rojas, J. S. Welsh and G. C. Goodwin, "A Receding Horizon Algorithm to Generate Binary Signals with a Prescribed Autocovariance," in 2007 AMERICAN CONTROL CONFERENCE, VOLS 1-13, 2007, pp. 122-127.
[200]
J. S. Welsh and C. R. Rojas, "Frequency Localising Basis Functions for Wide-band System Identification : A Condition Number Bound for Output Error Systems," in Proceedings of the European Control Conference (ECC), 2007, pp. 4618-4624.
[201]
C. R. Rojas et al., "Optimal Experiment Design with Diffuse Prior Information," in Proceedings of the European Control Conference (ECC), 2007, pp. 935-940.
[202]
J. S. Welsh, C. R. Rojas and S. D. Mitchell, "Wideband parametric identification of a power transformer," in 2007 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING, VOLS 1-2, 2007, pp. 232-237.
[203]
R. A. Rojas and C. R. Rojas, "The inverse of sampling revisited," in Proceedings of the IASTED Conference on Intelligent Systems and Control (ISC-2001), 2001.
Chapters in books
[204]
C. R. Rojas and M. I. Müller, "Algorithms for data-driven H∞-norm estimation," in Data-driven filter and control design : Methods and applications, Carlo Novara and Simone Formentin Ed., : IET Digital Library, 2019, pp. 145-163.
[205]
C. R. Rojas et al., "Open-cut Mine Planning via Closed-loop Receding-horizon Optimal Control," in Identification and Control : The Gap between Theory and Practice, Ricardo S. Sánchez-Peña, Joseba Quevedo Casín and Vicenc Puig Cayuela Ed., London : Springer, 2007, pp. 43-62.
Non-peer reviewed
Articles
[206]
H. Hjalmarsson, C. R. Rojas and D. E. Rivera, "System identification : A Wiener-Hammerstein benchmark," Control Engineering Practice, vol. 20, no. 11, pp. 1095-1096, 2012.
Chapters in books
[207]
C. R. Rojas and M. I. Müller, "Algorithms for data-driven H∞-norm estimation," in Data-Driven Modeling, Filtering and Control, : Institution of Engineering and Technology (IET), 2019, pp. 145-163.
[208]
G. C. Goodwin, C. R. Rojas and J. S. Welsh, "Good, Bad and Optimal Experiments for Identification," in Forever Ljung in System Identification - Workshop on the occasion of Lennart Ljung’s 60th birthday, Torkel Glad and Gustaf Hendeby Ed., Lund, Sweden : Studentlitteratur, 2006.
Other
[209]
R. A. González et al., "Consistent identification of continuous-time systems under multisine input signal excitation," (Manuscript).
[210]
S. Pan et al., "Efficiency Analysis of the Simplified Refined Instrumental Variable Method for Continuous-time Systems," (Manuscript).
[211]
N. Everitt et al., "Variance Analysis of Linear SIMO Models with Spatially Correlated Noise," (Manuscript).
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