[1] I. Markovsky. The behavioral toolbox. In Proceedings of Machine Learning Research, volume 242, pages 130--141, 2024. [ bib | pdf | software ]
[2] J. Wang, L. Hemelhof, I. Markovsky, and P. Patrinos. A trust-region method for data-driven iterative learning control of nonlinear systems. In Conference on Decision and Control, 2024. [ bib | pdf ]
[3] A. Fazzi and I. Markovsky. Distance problems in the behavioral setting. In European Control Conference, pages 2021--2026, 2023. [ bib | Abstract ]
[4] L. Hemelhof, I. Markovsky, and P. Patrinos. Data-driven output matching of output-generalized bilinear and linear parameter-varying systems. In European Control Conference, pages 1525--1530, 2023. [ bib | Abstract ]
[5] V. Mishra, I. Markovsky, A. Fazzi, and P. Dreesen. Data-driven simulation for NARX systems. In Proc. of the European Association for Signal Processing, 2021. [ bib | DOI | Abstract ]
[6] I. Markovsky. System theory without transfer functions and state-space? Yes, it's possible! In 60th IEEE Conference on Decision and Control, 2021. [ bib | DOI | pdf | Abstract ]
[7] Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky, and Konstantin Usevich. Common dynamic estimation via structured low-rank approximation with multiple rank constraints. In 19th IFAC Symposium on System Identification, volume 54, pages 103--107, 2021. [ bib | DOI | Abstract ]
[8] D. Verbeke and I. Markovsky. Line spectral estimation with palyndromic kernels. In In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages 5960--5963, Barcelona, 2020. [ bib | DOI | .pdf | Abstract ]
[9] V. Mishra, I. Markovsky, and B. Grossmann. Data-driven tests for controllability. In 59th IEEE Conference on Decision and Control, 2020. [ bib | pdf ]
[10] A. Fazzi, N. Guglielmi, and I. Markovsky. Computing common factors of matrix polynomials with applications in system and control theory. In Proc. of the IEEE Conf. on Decision and Control, pages 7721--7726, Nice, France, December 2019. [ bib | DOI | pdf | Abstract ]
[11] K. Usevich and I. Markovsky. Software package for mosaic-Hankel structured low-rank approximation. In Proc. of the IEEE Conf. on Decision and Control, pages 7165--7170, Nice, France, December 2019. [ bib | DOI | .pdf | Abstract ]
[12] P. Dreesen and I. Markovsky. Data-driven simulation using the nuclear norm heuristic. In In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Brighton, UK, 2019. [ bib | DOI | pdf | Abstract ]
[13] I. Markovsky, T. Liu, and A. Takeda. Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. In Proc. of the IEEE Conf. on Decision and Control, pages 2672--2675, 2019. [ bib | DOI | pdf | software | Abstract ]
[14] S. Formentin and I. Markovsky. A comparison between structured low-rank approximation and correlation approach for data-driven output tracking. In Proc. of the IFAC Symposium on System Identification, pages 1068--1073, 2018. [ bib | DOI | pdf | Abstract ]
[15] M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Ultrasound signal reconstruction from sparse samples using a low-rank and joint-sparse model. In In Proceedings of iTWIST'18, Paper-ID: 21, Marseille, France, 2018. [ bib | DOI | pdf | Abstract ]
[16] I. Markovsky, O. Debals, and L. De Lathauwer. Sum-of-exponentials modeling and common dynamics estimation using tensorlab. In 20th World Congress of the International Federation of Automatic Control, pages 14715--14720, Toulouse, France, July 2017. [ bib | DOI | pdf | Abstract ]
[17] I. Markovsky. Application of low-rank approximation for nonlinear system identification. In 25th IEEE Mediterranean Conference on Control and Automation, pages 12--16, Valletta, Malta, July 2017. [ bib | DOI | pdf | Abstract ]
[18] G. Mercèr, I. Markovsky, and J. Ramos. Innovation-based subspace identification in open- and closed-loop. In Proc. of the 55th IEEE Conference on Decision and Control, Las Vegas, USA, December 2016. [ bib | DOI | pdf | software | Abstract ]
[19] I. Markovsky and N. Guglielmi. Model order estimation based on a method for computing distance to uncontrollability. In Proc. of the Conference on Noise and Vibration Engineering (ISMA), pages 2963--2970, Leuven, Belgium, September 2016. [ bib | pdf | Abstract ]
[20] I. Markovsky and R. Pintelon. Consistent estimation of autonomous linear time-invariant systems from multiple experiments. In Proc. of the Conference on Noise and Vibration Engineering (ISMA), pages 3265--3268, Leuven, Belgium, September 2014. [ bib | pdf | Abstract ]
[21] M. Ishteva and I. Markovsky. Tensor low multilinear rank approximation by structured matrix low-rank approximation. In Proc. of the 21st International Symposium on Mathematical Theory of Networks and Systems, pages 1808--1812, Groningen, The Netherlands, July 2014. [ bib | pdf | Abstract ]
[22] I. Markovsky. Approximate identification with missing data. In Proc. of the 52nd IEEE Conference on Decision and Control, pages 156--161, Florence, Italy, December 2013. [ bib | DOI | pdf | software | Abstract ]
[23] I. Markovsky. Exact identification with missing data. In Proc. of the 52nd IEEE Conference on Decision and Control, pages 151--155, Florence, Italy, 2013. [ bib | DOI | pdf | software | Abstract ]
[24] I. Markovsky. Dynamical systems and control mindstorms. In Proc. 20th Mediterranean Conf. on Control and Automation, pages 54--59, Barcelona, 2012. [ bib | DOI | pdf | Abstract ]
[25] K. Usevich and I. Markovsky. Structured low-rank approximation as a rational function minimization. In Proc. of the 16th IFAC Symposium on System Identification, pages 722--727, Brussels, 2012. [ bib | DOI | pdf | Abstract ]
[26] I. Markovsky. How effective is the nuclear norm heuristic in solving data approximation problems? In Proc. of the 16th IFAC Symposium on System Identification, pages 316--321, Brussels, 2012. [ bib | DOI | pdf | software | Abstract ]
[27] F. Le, I. Markovsky, C. Freeman, and E. Rogers. Recursive identification of Hammerstein structure. In Proc. of the 18th IFAC World Congress, volume 44, pages 13954--13959, Milano, Italy, August 2011. [ bib | DOI ]
[28] F. Le, I. Markovsky, C. Freeman, and E. Rogers. Online identification of electrically stimulated muscle models. In Proc. of the American Control Conference (ACC), pages 90--95, San Francisco, USA, June 2011. [ bib | DOI ]
[29] F. Le, I. Markovsky, C. Freeman, and E. Rogers. Identification of electrically stimulated muscle after stroke. In European Control Conference, pages 1576--1581, Budapest, Hungary, August 2009. [ bib | DOI ]
[30] I. Markovsky. An algorithm for closed-loop data-driven simulation. In 15th IFAC Symposium on System Identification, pages 114--115, Saint-Malo, France, July 2009. [ bib | DOI | pdf | software | Abstract ]
[31] I. Markovsky. Applications of structured low-rank approximation. In 15th IFAC Symposium on System Identification, pages 1121--1126, Saint-Malo, France, July 2009. [ bib | DOI ]
[32] M. Przedwojski, I. Markovsky, and E. Rogers. Identifiability of clock synchronization errors: a behavioural approach. In 48th IEEE Conf. on Decision and Control, pages 8095--8100, Shanghai, China, 2009. [ bib | DOI | pdf | Abstract ]
[33] I. Markovsky, A. Amann, and S. Van Huffel. Application of filtering methods for removal of resuscitation artifacts from human ECG signals. In Proc. of the 30th Conf. of IEEE Eng. in Medicine and Biology Soc. (EMBS), pages 13--16, Vancouver, Canada, August 2008. [ bib | DOI | pdf | Abstract ]
[34] P. Rapisarda and I. Markovsky. Why "state" feedback? In Proc. of the 17th IFAC World Congress, pages 12285--12290, Seoul, Korea, July 2008. [ bib | DOI | pdf | Abstract ]
[35] I. Markovsky and S. Rao. Palindromic polynomials, time-reversible systems, and conserved quantities. In 16th Mediterranean Conf. on Control and Automation, pages 125--130, Ajaccio, France, June 2008. [ bib | DOI | pdf | Abstract ]
[36] I. Markovsky and P. Rapisarda. On the linear quadratic data-driven control. In Proc. of the European Control Conf., pages 5313--5318, Kos, Greece, July 2007. [ bib | DOI | pdf | Abstract ]
[37] I. Markovsky, J. C. Willems, and B. De Moor. Software for exact linear system identification. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 1475--1483, Kyoto, Japan, 2006. [ bib | DOI | pdf | software | Abstract ]
[38] I. Markovsky, A. Kukush, and S. Van Huffel. On errors-in-variables estimation with unknown noise variance ratio. In Proc. of the 14th IFAC Symp. on System Identification, pages 172--177, Newcastle, Australia, 2006. [ bib | DOI | pdf | Abstract ]
[39] I. Markovsky, J. C. Willems, and B. De Moor. Recursive computation of the most powerful unfalsified model. In In Proc. of the of the 14th IFAC Symp. on System Identification, pages 588--593, Newcastle, Australia, 2006. [ bib | DOI | software | .ps.gz | Abstract ]
[40] J. C. Willems, I. Markovsky, and B. De Moor. State construction in subspace identification. In Proc. of the 14th IFAC Symposium on System Identification, pages 303--308, Newcastle, Australia, 2006. [ bib | DOI | .ps.gz | Abstract ]
[41] I. Markovsky, J. C. Willems, and B. De Moor. Comparison of identification algorithms on the database for system identification DAISY. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 2858--2869, Kyoto, Japan, 2006. [ bib | pdf ]
[42] I. Markovsky and S. Van Huffel. An algorithm for approximate common divisor computation. In Proc. of the 17th Symp. on Math. Theory of Networks and Systems, pages 274--279, Kyoto, Japan, 2006. [ bib | pdf ]
[43] I. Markovsky, J. C. Willems, and B. De Moor. The module structure of ARMAX systems. In Proc. of the 41st Conf. on Decision and Control, pages 811--816, San Diego, USA, 2006. [ bib | DOI | .ps.gz | Abstract ]
[44] I. Markovsky, J. Boets, B. Vanluyten, K. De Cock, and B. De Moor. When is a pole spurious? In Proc. of the International Conf. on Noise and Vibration Engineering, pages 1615--1626, Leuven, Belgium, 2006. [ bib | .ps.gz | Abstract ]
[45] I. Markovsky, J. C. Willems, P. Rapisarda, and B. De Moor. Data driven simulation with applications to system identification. In Proc. of the 16th IFAC World Congress, Prague, Czech Republic, 2005. [ bib | DOI | pdf | software ]
[46] I. Markovsky, J. C. Willems, and B. De Moor. State representations from finite time series. In Proc. of the 44th Conf. on Decision and Control, pages 832--835, Seville, Spain, 2005. [ bib | DOI | pdf ]
[47] I. Markovsky, J. C. Willems, S. Van Huffel, and B. De Moor. Software for approximate linear system identification. In Proc. of the 44th Conf. on Decision and Control, pages 1559--1564, Seville, Spain, 2005. [ bib | DOI | pdf | software ]
[48] I. Markovsky, S. Van Huffel, and B. De Moor. H2-optimal linear parametric design. In Proc. of the 16th Int. Symp. on Math. Theory of Networks and Systems, 2004. [ bib | DOI | software | .pdf ]
[49] I. Markovsky, J. C. Willems, S. Van Huffel, B. De Moor, and R. Pintelon. Application of structured total least squares for system identification. In Proc. of the 43rd Conf. on Decision and Control, pages 3382--3387, Atlantis, Paradise Island, Bahamas, 2004. [ bib | DOI | pdf ]
[50] J. C. Willems, I. Markovsky, P. Rapisarda, and B. De Moor. A note on persistency of excitation. In Proc. of the 43rd Conf. on Decision and Control, pages 2630--2631, Atlantis, Paradise Island, Bahamas, 2004. [ bib | DOI | pdf ]
[51] I. Markovsky and B. De Moor. Linear dynamic filtering with noisy input and output. In Proc. of the 13th IFAC Symp. on System Identification, pages 1749--1754, Rotterdam, The Netherlands, 2003. [ bib | DOI | pdf ]
[52] I. Markovsky, S. Van Huffel, and B. De Moor. Multi-model system parameter estimation. In CD-ROM proceedings of IEEE Int. Conf. on Systems, Man, and Cybernetics, 2002. [ bib | DOI | pdf ]
[53] I. Markovsky, J. C. Willems, and B. De Moor. Continuous-time errors-in-variables filtering. In Proc. of the 41st Conf. on Decision and Control, pages 2576--2581, Las Vegas, NV, 2002. [ bib | DOI | pdf ]

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