Publications


Also check my Google Scholar Profile, or find me on ResearchGate!

Journal articles

[1] P. Dreesen, K. Batselier, and B. De Moor. Multidimensional realization theory and polynomial system solving. Int. J. Control, 2018. [ bib | DOI | pdf ]
[2] A. Fakhrizadeh Esfahani, P. Dreesen, K. Tiels, J.-P. Noël, and J. Schoukens. Parameter reduction in nonlinear state-space identification of hysteresis. Mech. Syst. Signal Process., 104:884--895, 2018. [ bib | DOI | pdf ]
[3] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. Approximate decoupling of multivariate polynomials using weighted tensor decomposition. Numer. Lin. Alg. Appl., 25(2), 2018. [ bib | DOI | pdf ]
[4] R. Relan, K. Tiels, A. Marconato, P. Dreesen, and J. Schoukens. Data-driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record. Mech. Syst. Signal Process., 104:929--943, 2018. [ bib | DOI | pdf ]
[5] K. Usevich, P. Dreesen, and M. Ishteva. Decoupling multivariate polynomials: interconnections between tensorizations. arXiv:1703.02493, 2017. [ bib ]
[6] P. Dreesen, M. Ishteva, and J. Schoukens. Decoupling multivariate polynomials using first-order information and tensor decompositions. SIAM J. Matrix Anal. Appl., 36(2):864--879, 2015. [ bib | DOI | pdf ]
[7] K. Batselier, P. Dreesen, and B. De Moor. On the null spaces of the Macaulay matrix. Lin. Alg. Appl., 460(1):259--289, 2014. [ bib | DOI | pdf ]
[8] K. Batselier, P. Dreesen, and B. De Moor. The canonical decomposition of Cdn and numerical Gröbner border bases. SIAM J. Mat. Anal. Appl., 35(4):1242--1264, 2014. [ bib | DOI | pdf ]
[9] K. Batselier, P. Dreesen, and B. De Moor. A fast recursive orthogonalization scheme for the Macaulay matrix. J. Comp. Appl. Math., 267:20--32, 2014. [ bib | DOI | pdf ]
[10] K. Batselier, P. Dreesen, and B. De Moor. A geometrical approach to finding multivariate approximate LCMs and GCDs. Lin. Alg. Appl., 438(9):3618--3628, May 2013. [ bib | DOI | pdf ]
[11] K. Batselier, P. Dreesen, and B. De Moor. The geometry of multivariate polynomial division and elimination. SIAM J. Mat. Anal. Appl., 34(1):102--125, 2013. [ bib | DOI | pdf ]
[12] T. Falck, P. Dreesen, K. De Brabanter, K. Pelckmans, B. De Moor, and J. A. K. Suykens. Least-squares support vector machines for the identification of Wiener-Hammerstein systems. Control Eng. Pract., 20:1165--1174, 2012. [ bib | DOI | pdf ]

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Conference proceedings

[1] P. Dreesen, J. De Geeter, and M. Ishteva. Decoupling multivariate functions using second-order information and tensors. In Proc. 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018), Guildford, UK, 2018. [ bib | pdf ]
[2] P. Dreesen, K. Tiels, M. Ishteva, and J. Schoukens. Nonlinear system identification: finding structure in nonlinear black-box models. In Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017), pages 443--446, 2017. [ bib | DOI | pdf ]
[3] P. Dreesen, D.T. Westwick, M. Ishteva, and J. Schoukens. Modeling parallel Wiener-Hammerstein systems using tensor decomposition of Volterra kernels. In P. Tichavsky, M. Babaie-Zadeh, O. Michel, and N. Thirion-Moreau, editors, Proc. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), volume 10169 of Lecture Notes on Computer Science (LNCS), pages 16--25, Grenoble, France, 2017. [ bib | DOI | pdf ]
[4] A. Fakhrizadeh Esfahani, P. Dreesen, K. Tiels, J.-P. Noël, and J. Schoukens. Polynomial state-space model decoupling for the identification of hysteretic systems. In Proc. IFAC 2017 World Congress, volume 50(1) of IFAC-PapersOnLine, pages 458--463, Toulouse, France, 2017. [ bib | DOI | pdf ]
[5] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. An initialization method for nonlinear model reduction. In P. Tichavsky, M. Babaie-Zadeh, O. Michel, and N. Thirion-Moreau, editors, Proc. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), volume 10169 of Lecture Notes on Computer Science (LNCS), pages 111--120, Grenoble, France, 2017. [ bib | DOI | pdf ]
[6] D.T. Westwick, P. Dreesen, M. Ishteva, and J. Schoukens. Tensor factorization based estimates of parallel Wiener-Hammerstein models. In Proc. 20th IFAC World Congress (IFAC 2017), volume 50(1) of IFAC-PapersOnLine, pages 9468--9473, Toulouse, France, 2017. [ bib | DOI | pdf ]
[7] P. Dreesen, A. Fakhrizadeh Esfahani, J. Stoev, K. Tiels, and J. Schoukens. Decoupling nonlinear state-space models: case studies. In P. Sas, D. Moens, and A. van de Walle, editors, International Conference on Noise and Vibration (ISMA2016) and International Conference on Uncertainty in Structural Dynamics (USD2016), pages 2639--2646, Leuven, Belgium, 2016. [ bib | pdf ]
[8] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. Parallel Wiener-Hammerstein identification: a case study. In P. Sas, D. Moens, and A. van de Walle, editors, International Conference on Noise and Vibration (ISMA2016) and International Conference on Uncertainty in Structural Dynamics (USD2016), pages 2647--2656, Leuven, Belgium, 2016. [ bib | pdf ]
[9] P. Dreesen, T. Goossens, M. Ishteva, L. De Lathauwer, and J. Schoukens. Block-decoupling multivariate polynomials using the tensor block-term decomposition. In E. Vincent, A. Yeredor, Z. Koldovsky, and P. Tichavsky, editors, Proc. 12th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2015), volume 9237 of Lecture Notes on Computer Science (LNCS), pages 14--21, Liberec, Czech Republic, 2015. [ bib | pdf ]
[10] P. Dreesen, M. Ishteva, and J. Schoukens. Recovering Wiener-Hammerstein nonlinear state-space models using linear algebra. In Proc. 17th IFAC Symposium on System Identification (SYSID 2015), volume 48(28) of IFAC-PapersOnLine, pages 951--956, Beijing, China, 2015. [ bib | DOI | pdf ]
[11] P. Dreesen, M. Schoukens, K. Tiels, and J. Schoukens. Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach. In Proc. 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2015), pages 987--992, Pisa, Italy, 2015. [ bib | pdf ]
[12] P. Dreesen, M. Ishteva, and J. Schoukens. On the full and block-decoupling of nonlinear functions. In PAMM-Proceedings of Applied Mathematics and Mechanics, volume 15(1), pages 739--742, 2015. [ bib | DOI | http ]
[13] K. Batselier, P. Dreesen, and B. De Moor. Maximum likelihood estimation and polynomial system solving. In Proc. 11th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), pages 369--374, 2012. [ bib | pdf ]
[14] K. Batselier, P. Dreesen, and B. De Moor. Prediction error method identification is an eigenvalue problem. In Proc. 16th IFAC Symposium on System Identification (SYSID 2012), volume 45(16) of IFAC Proceedings Volumes, pages 221--226, Brussels, Belgium, 2012. [ bib | DOI | pdf ]
[15] P. Dreesen, K. Batselier, and B. De Moor. Weighted/structured total least squares problems and polynomial system solving. In Proc. 11th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), pages 351--356, Bruges, Belgium, 2012. [ bib | pdf ]
[16] P. Dreesen, K. Batselier, and B. De Moor. Back to the roots: polynomial system solving, linear algebra, systems theory. In Proc. 16th IFAC Symposium on System Identification (SYSID 2012), volume 45(16) of IFAC Proceedings Volumes, pages 1203--1208, Brussels, Belgium, 2012. [ bib | DOI | pdf ]
[17] D. Geebelen, K. Batselier, P. Dreesen, M. Signoretto, J. A. K. Suykens, B. De Moor, and J. Vandewalle. Joint regression and linear combination of time series for optimal prediction. In Proc. 11th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), pages 357--362, Bruges, Belgium, 2012. [ bib | pdf ]
[18] K. De Brabanter, P. Dreesen, P. Karsmakers, K. Pelckmans, J. De Brabanter, J. A. K. Suykens, and B. De Moor. Fixed-size LS-SVM applied to the Wiener-Hammerstein benchmark. In Proc. 15th IFAC Symposium on System Identifcation (SYSID 2009), volume 42(10) of IFAC Proceedings Volumes, pages 826--831, Saint-Malo, France, 2009. [ bib | DOI | pdf | http ]

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Book chapters

[1] P. Dreesen and B. De Moor. Polynomial optimization problems are eigenvalue problems. In P. M. J. Van den Hof, C. Scherer, and P. S. C. Heuberger, editors, Model-Based Control -- Bridging Rigorous Theory and Advanced Technology, pages 49--68. Springer, 2009. [ bib | pdf ]

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PhD thesis

[1] P. Dreesen. Back to the Roots -- Polynomial System Solving Using Linear Algebra. PhD thesis, Faculty of Engineering Science, KU Leuven, Leuven, Belgium, 2013. [ bib | pdf ]

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