[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 ]

This file was generated by bibtex2html 1.98.