Publications

A publication list with download links can be found below. Also check out my Google Scholar profile.

[1] 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. [ .pdf ]
[2] P. Dreesen, D. Westwick, J. Schoukens, and M. Ishteva. Modeling parallel Wiener-Hammerstein systems using tensor decomposition of Volterra kernels, 2016. [ arXiv | .pdf ]
[3] 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. [ .pdf ]
[4] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. Weighted tensor decomposition for approximate decoupling of multivariate polynomials, 2016. [ arXiv | .pdf ]
[5] 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, 2015. [ .pdf ]
[6] P. Dreesen, K. Batselier, and B. De Moor. On polynomial system solving and multidimensional realization theory. Technical Report 15-145, KU Leuven Department of Electrical Engineering ESAT/Stadius, 2015. [ .pdf ]
[7] P. Dreesen, M. Ishteva, and J. Schoukens. Recovering Wiener-Hammerstein nonlinear state-space models using linear algebra. IFAC-PapersOnLine, 48(28):951--956, 2015. [ DOI | .pdf ]
[8] 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. [ .pdf ]
[9] 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. [ .pdf ]
[10] 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, pages 739--742, 2015. [ DOI | http ]
[11] K. Batselier, P. Dreesen, and B. De Moor. On the null spaces of the Macaulay matrix. Lin. Alg. Appl., 460(1):259--289, 2014. [ .pdf ]
[12] 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. [ .pdf ]
[13] 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. [ .pdf ]
[14] 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. [ .pdf ]
[15] 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. [ .pdf ]
[16] P. Dreesen. Back to the Roots -- Polynomial System Solving Using Linear Algebra. PhD thesis, Faculty of Engineering Science, KU Leuven, Leuven, Belgium, 2013. [ .pdf ]
[17] K. Batselier, P. Dreesen, and B. De Moor. Maximum likelihood estimation and polynomial system solving. In 11th Eur. Symp. Artif. Neur. Netw., Comput. Intell. Mach. Learn. (ESANN 2012), pages 369--374, 2012. [ .pdf ]
[18] K. Batselier, P. Dreesen, and B. De Moor. Prediction error method identification is an eigenvalue problem. IFAC Proceedings Volumes, 45(16):221--226, 2012. [ DOI | .pdf ]
[19] P. Dreesen, K. Batselier, and B. De Moor. Weighted/structured total least squares problems and polynomial system solving. In 11th Eur. Symp. Artif. Neur. Netw., Comput. Intell. Mach. Learn. (ESANN 2012), pages 351--356, 2012. [ .pdf ]
[20] P. Dreesen, K. Batselier, and B. De Moor. Back to the roots -- polynomial system solving using linear algebra. Technical Report 12-169, KU Leuven Department of Electrical Engineering ESAT/SCD, 2012. [ .pdf ]
[21] P. Dreesen, K. Batselier, and B. De Moor. Back to the roots: polynomial system solving, linear algebra, systems theory. IFAC Proceedings Volumes, 45(16):1203--1208, 2012. [ DOI | .pdf ]
[22] 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. [ .pdf ]
[23] 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 Eur. Symp. Artif. Neur. Netw., Comput. Intell. Mach. Learn. (ESANN 2012), pages 357--362, 2012. [ .pdf ]
[24] 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. [ .pdf ]
[25] 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. IFAC Proceedings Volumes, 42(10):826--831, 2009. [ DOI | .pdf | http ]

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