Overview
Software
Publications
Presentations

Publications of the DECOUPLE project

Books

[1] I. Markovsky. Low-Rank Approximation: Algorithms, Implementation, Applications. Communications and Control Engineering. Springer, second edition edition, 2019.

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PhD theses

[1] G. Hollander. Multivariate polynomial decoupling in nonlinear system identification. PhD thesis, Vrije Universiteit Brussel (VUB), 2017. [ .html ]

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Journal papers

[1] K. Usevich, P. Dreesen, and M. Ishteva. Decoupling multivariate polynomials: interconnections between tensorizations. J. Comp. Appl. Math. (in press), 2019. (preprint at arXiv:1703.02493). [ bib | DOI ]
[2] J. Decuyper, P. Dreesen, J. Schoukens, M. C. Runacres, and K. Tiels. Decoupling multivariate polynomials for nonlinear state-space models. IEEE Control Systems Letters (L-CSS) (in press), 2019. [ bib | DOI ]
[3] A. Fakhrizadeh Esfahani, P. Dreesen, K. Tiels, J.-P. Noël, and J. Schoukens. Parameter reduction in nonlinear state-space identification of hysteresis. Mechanical Systems and Signal Processing, 104:884--895, 2018. [ bib | DOI ]
[4] P. Dreesen, K. Batselier, and B. De Moor. Multidimensional realization theory and polynomial system solving. Int. J. Control, 91(12):2692--2704, 2018. [ bib | DOI ]
[5] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. Approximate decoupling of multivariate polynomials using weighted tensor decomposition. Numerical Linear Algebra with Applications, 25(2):e2135, 2018. [ bib | DOI ]
[6] A. Fazzi, N. Guglielmi, and I. Markovsky. An ODE based method for computing the approximate greatest common divisor of polynomials. Numerical algorithms, 2018. [ bib | DOI | pdf | Abstract ]
[7] 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 ]
[8] N. Guglielmi and I. Markovsky. An ODE based method for computing the distance of co-prime polynomials to common divisibility. SIAM Journal on Numerical Analysis, 55:1456--1482, 2017. [ bib | DOI | pdf | Abstract ]
[9] 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 | Abstract ]

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Conference papers

[1] P. Dreesen, J. De Geeter, and M. Ishteva. Decoupling multivariate functions using second-order information and tensors. In Y. Deville, S. Gannot, R. Mason, M. D. Plumbley, and D. Ward, editors, Proc. 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018), volume 10891 of Lecture Notes on Computer Science (LNCS), pages 79--88, Guildford, UK, 2018. [ bib | DOI | http ]
[2] I. Markovsky, O. Debals, and L. De Lathauwer. Sum-of-exponentials modeling and common dynamics estimation using tensorlab. In Proc. 20th IFAC World Congress, pages 14715--14720, Toulouse, France, July 2017. [ bib | pdf | Abstract ]
[3] I. Markovsky. Application of low-rank approximation for nonlinear system identification. In Proc. 25th IEEE Mediterranean Conf. on Control and Automation, pages 12--16, Valletta, Malta, July 2017. [ bib | pdf | Abstract ]
[4] P. Dreesen, K. Tiels, M. Ishteva, and J. Schoukens. Nonlinear system identification: finding structure in nonlinear black-box models. In Proc. IEEE Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pages 443--446, 2017. [ bib ]
[5] D. Westwick, M. Ishteva, P. Dreesen, and J. Schoukens. Tensor factorization based estimates of parallel Wiener-Hammerstein models. In Proc. IFAC World Congress, volume 50, pages 9468--9473, 2017. [ bib ]
[6] 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 ]
[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, Int. Conf. on Noise and Vibration, Leuven, Belgium, pages 2639--2646, 2016. [ bib ]
[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, Int. Conf. on Noise and Vibration, pages 2647--2656, 2016. [ bib ]
[9] 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, volume 48(28), pages 951--956, Beijing, China, 2015. [ bib | DOI | 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. [ bib | DOI | pdf | http ]
[11] P. Dreesen, M. Ishteva, and J. Schoukens. Recovering Wiener-Hammerstein nonlinear state-space models using linear algebra. In Proc. IFAC World Congress, volume 48, pages 951--956, Beijing, China, 2015. [ bib | DOI ]
[12] P. Dreesen, M. Schoukens, K. Tiels, and J. Schoukens. Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach. In Proc. IEEE Int. Conf. on Instrumentation and Measurement Technology, pages 987--992, 2015. [ bib ]
[13] K. Usevich. Decomposing multivariate polynomials with structured low-rank matrix completion. In Proc. 21th Int. Symposium on Mathematical Theory of Networks and Systems, pages 1826--1833, 2014. [ bib | pdf | Abstract ]
[14] A. Van Mulders, L. Vanbeylen, and K. Usevich. Identification of a block-structured model with several sources of nonlinearity. In Proc. 14th European Control Conf., pages 1717--1722, 2014. [ bib | DOI | pdf | Abstract ]

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Book chapters

[1] I. Markovsky, A. Fazzi, and N. Guglielmi. Applications of polynomial common factor computation in signal processing. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 99--106. Springer, 2018. [ bib | DOI | pdf | Abstract ]
[2] I. Markovsky and P.-L. Dragotti. Using structured low-rank approximation for sparse signal recovery. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 479--487. Springer, 2018. [ bib | DOI | pdf | software | Abstract ]
[3] G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens. An initialization method for nonlinear model reduction. In Latent Variable Analysis and Signal Separation, volume 10169 of Lecture Notes on Computer Science, pages 111--120. 2017. [ bib ]
[4] P. Dreesen, D. T. Westwick, J. Schoukens, and M. Ishteva. Modeling parallel Wiener-Hammerstein systems using tensor decomposition of Volterra kernels. In Latent Variable Analysis and Signal Separation, volume 10169 of Lecture Notes in Computer Science, pages 16--25. 2017. [ bib ]
[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. Koldovský, and P. Tichavský, editors, Latent Variable Analysis and Signal Separation, volume 9237 of Lecture Notes in Computer Science, pages 14--21. Springer, 2015. [ bib | DOI | pdf | http ]

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Technical reports

[1] K. Usevich and I. Markovsky. Software package for mosaic-hankel structured low-rank approximation. Technical report, Dept. ELEC, Vrije Universiteit Brussel, 2017. [ bib | .pdf | Abstract ]

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