Introduction to the Content

This page collects the abstracts and presentations that were part of the different editions of the Workshop on Nonlinear System Identification Benchmarks, the results of recent invited sessions based upon the benchmarks featured on this website, and benchmark results published in other journal or conference papers. You can find the different, keynotes, and regular talks of the past workshops, the list of invited sessions, and the papers published on the different benchmarks below.

Is your publication missing on this page? Would you want to feature additional material (slides, code, toolbox, ...)? Please contact us and we will include your research results on this webpage!

Workshop Keynotes


2017

  1. David Barton, University of Bristol
          Keynote title: Control-based continuation - from models to experiments, slides
  2. Lennart Ljung, Linköping University
          Keynote title: Non-linear system identification: A palette from off-white to pit-black, slides
  3. Carl Edward Rasmussen and Johan Schoukens, University of Cambridge and Vrije Universiteit Brussel
          Keynote title: Bayesians methods in system identification: equivalences, differences, and misunderstanding,
          updated slides, slides 26/04/2017
  4. Bart Peeters, Siemens PLM Software
          Keynote title: Structural non-linearities – an industrial view, slides

2016

  1. Gaëtan Kerschen, Université de Liège
          Keynote title: Identification of Nonlinear Mechanical Systems: State of the Art and Recent Trends, slides
  2. Carl Edward Rasmussen, University of Cambridge
          Keynote title: Variational Inference in Gaussian Processes for Non-Linear Time Series, slides
  3. Thomas Schön, Uppsala Universitet
          Keynote title: Solving Nonlinear Inference Problems using Sequential Monte Carlo, slides
  4. Johan Schoukens, Vrije Universiteit Brussel
          Keynote title: Data Driven Discrete Time Modeling of Continuous Time Nonlinear Systems: Problems, Challenges, Success Stories, slides
  5. Keith Worden, The University of Sheffield
          Keynote title: Is System Identification Just Machine Learning?, slides

Workshop Regular Talks


2017

The complete book of abstracts of the 2017 Workshop on Nonlinear System Identification Benchmarks can be found here. Some of the presentations, sometimes completed by the code used to generate the presented results, can be found below.

  1. T. Dossogne, J.P. Noël and G. Kerschen, Nonlinear system identification of an F-16 aircraft using the acceleration surface method, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, toolbox
  2. K. Tiels, Polynomial nonlinear state-space modeling of the F-16 aircraft benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  3. P. Dreesen, K. Tiels and M. Ishteva, Decoupling nonlinear models for the F-16 ground vibration test benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017.
  4. G. Hollander, P. Dreesen, M. Ishteva and J. Schoukens, Nonlinear model decoupling using a tensor decomposition initialization, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  5. T. Münker, T.O. Heinz and O. Nelles, Regularized local FIR model networks for a Bouc-Wen and a Wiener-Hammerstein system, Workshop on Nonlinear System Identification Benchmarks, 2017.
  6. E. Zhang and M. Schoukens, Fast location of process noise for nonlinear system identification, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, Matlab script
    Readme in the Matlab script.
  7. B. Tang, M.J. Brennan and G. Gatti, On the interaction of an electro-dynamic shaker and a beam with stiffness nonlinearity, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  8. G. Giordano and J. Sjöberg, Maximum likelihood identification of Wiener-Hammerstein models in presence of process noise, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  9. R. Relan, D. Verbeke and K. Tiels, One step ahead prediction of the WH benchmark with process noise using kernel adaptive learning, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  10. M. Rébillat and M. Schoukens, A methodology to compare two estimation methods for parallel Hammerstein models, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  11. L. Ljung, Matlab System Identification Toolbox demonstration, Workshop on Nonlinear System Identification Benchmarks, 2017. toolbox, Matlab script
    Note that the Matlab script requires Matlab2016b or higher to work, also the F-16 ground vibration test benchmark data should be downloaded.
  12. M. Schoukens, Interpolated linear modeling of the F16 benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, Matlab script
    Readme in the Matlab script.
  13. P.Z. Csurcsia, G. Birpoutsoukis and J. Schoukens, Transient elimination and memory saving possibilities for multidimensional nonparametric regularization illustrated on the cascaded water tanks benchmark problem, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  14. S.R. Hassan, System identification of dynamic force transducers, Workshop on Nonlinear System Identification Benchmarks, 2017.
  15. A.F. Esfahani, P. Dreesen, J.P. Noël, K. Tiels, J. Schoukens, Decoupled polynomial nonlinear state space models of a Bouc-Wen hysteretic system, Workshop on Nonlinear System Identification Benchmarks, 2017.
  16. D. Westwick, G. Hollander and J. Schoukens, The decoupled polynomial NARX model: parameter reduction and structural insights for the Bouc-Wen benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides

2016

The complete book of abstracts of the 2016 Workshop on Nonlinear System Identification Benchmarks can be found here. Some of the presentations, sometimes completed by the code used to generate the presented results, will be posted below.

  1. K. Tiels, PNLSS 1.0 - A polynomial nonlinear state-space Matlab toolbox, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  2. A. Svensson, F. Lindsten, T.B. Schön, Particle methods for the Wiener-Hammerstein system, Workshop on Nonlinear System Identification Benchmarks, 2016.
  3. E. Zhang, M. Schoukens, J. Schoukens, Structural modeling of Wiener-Hammerstein system in the presence of the process noise, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  4. G. Holmes, T. Rogers, E.J. Cross, N. Dervilis, G. Manson, R.J. Barthorpe, K. Worden, Cascaded Tanks Benchmark: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  5. G. Giordano, J. Sjöberg, Cascade Tanks Benchmark, Workshop on Nonlinear System Identification Benchmarks, 2016.
  6. J.P. Noël, A.F. Esfahani, G. Kerschen, J. Schoukens, A nonlinear state-space solution to a hysteretic benchmark in system identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  7. A.F. Esfahani, P. Dreesen, K. Tiels, J.P. Noël, J. Schoukens, Using a polynomial decoupling algorithm for state-space identification of a Bouc-Wen system, Workshop on Nonlinear System Identification Benchmarks, 2016.
  8. R. Gaasbeek, R. Mohan, Control-focused identification of hysteric systems: Selecting model structures? Think about the final use of the model!, Workshop on Nonlinear System Identification Benchmarks, 2016.
  9. A. Bajrić, System identification of a linearized hysteretic system using covariance driven stochastic subspace identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  10. R. Relan, K. Tiels, A. Marconato, Identifying an Unstructured Flexible Nonlinear Model for the Cascaded Water-tanks Benchmark: Capabilities and Short-comings, Workshop on Nonlinear System Identification Benchmarks, 2016.
  11. P. Mattson, D. Zachariah, P. Stoica, Identification of a PWARX model for the cascade water tanks, Workshop on Nonlinear System Identification Benchmarks, 2016.
  12. G. Birpoutsoukis, P.Z. Csurcsia, Nonparametric Volterra series estimate of the cascaded tank, Workshop on Nonlinear System Identification Benchmarks, 2016.
  13. M. Rébillat, K. Ege, N. Mechbal, J. Antoni, Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties, Workshop on Nonlinear System Identification Benchmarks, 2016.
  14. M. Schoukens, Identification of Wiener-Hammerstein systems with process noise using an Errors-in-Variables framework, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  15. K. Worden, G. Manson, R.J. Barthorpe, E.J. Cross, N. Dervilis, G. Holmes, T. Rogers, Wiener-Hammerstein Benchmark with process noise: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  16. G. Manson, R.J. Barthorpe, E.J. Cross, N. Dervilis, G. Holmes, T. Rogers, K. Worden, Bouc-Wen Benchmark: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  17. E. Louarroudi, S. Vanlanduit, R. Pintelon, Identification of non-linear restoring forces through linear time-periodic approximations, Workshop on Nonlinear System Identification Benchmarks, 2016.
  18. M. Schoukens, F.G. Scheiwe, Modeling Nonlinear Systems Using a Volterra Feedback Model, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  19. A. Svensson, F. Lindsten, T.B. Schön, First principles and black box modeling of the cascaded water tanks, Workshop on Nonlinear System Identification Benchmarks, 2016.

Invited Sessions


IFAC World Congress 2017

An open invited track was organized at the 2017 IFAC World Congres in Toulouse, France. It featured the following talks:

  1. M. Schoukens, J.P. Noël, Three Benchmarks Addressing Open Challenges in Nonlinear System Identification, 20th World Congress The International Federation of Automatic Control, 2017, 448-453. slides
  2. R. Relan, K. Tiels, A. Marconato, J. Schoukens, An Unstructured Flexible Nonlinear Model for the Cascaded Water-Tanks Benchmark, 20th World Congress The International Federation of Automatic Control, 2017, 454-459.
  3. A. Fakhrizadeh Esfahani, P. Dreesen, K. Tiels, J.P. Noël, J. Schoukens, Polynomial State-Space Model Decoupling for the Identification of Hysteretic Systems, 20th World Congress The International Federation of Automatic Control, 2017, 460-465.
  4. M. Brunot, A. Janot, F. Carrillo, Continuous-Time Nonlinear Systems Identification with Output Error Method Based on Derivative-Free Optimisation, 20th World Congress The International Federation of Automatic Control, 2017, 466-471.
  5. J. Belz, T. Münker, T.O. Heinz, G. Kampmann, O. Nelles, Automatic Modeling with Local Model Networks for Benchmark Processes, 20th World Congress The International Federation of Automatic Control, 2017, 472-477.
  6. G. Birpoutsoukis, P.Z. Csurcsia, J. Schoukens, Nonparametric Volterra Series Estimate of the Cascaded Water Tanks Using Multidimensional Regularization, 20th World Congress The International Federation of Automatic Control, 2017, 478-483.

Publications on the Featured Benchmarks


F-16 Ground Vibration Test (2017)

  1. T. Dossogne, J.P. Noël and G. Kerschen, Nonlinear system identification of an F-16 aircraft using the acceleration surface method, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, toolbox
  2. K. Tiels, Polynomial nonlinear state-space modeling of the F-16 aircraft benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  3. P. Dreesen, K. Tiels and M. Ishteva, Decoupling nonlinear models for the F-16 ground vibration test benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017.
  4. L. Ljung, Matlab System Identification Toolbox demonstration, Workshop on Nonlinear System Identification Benchmarks, 2017. toolbox, Matlab script
    Note that the Matlab script requires Matlab2016b or higher to work, also the F-16 ground vibration test benchmark data should be downloaded.
  5. M. Schoukens, Interpolated linear modeling of the F16 benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, Matlab script
    Readme in the Matlab script.

Cascaded Tanks System (2016)

  1. A. Svensson, T.B. Schön, A flexible state space model for learning nonlinear dynamical systems, Automatica, 2017, 80, 189-199.
  2. M. Schoukens, J.P. Noël, Three Benchmarks Addressing Open Challenges in Nonlinear System Identification, 20th World Congress The International Federation of Automatic Control, 2017, 448-453. slides
  3. R. Relan, K. Tiels, A. Marconato, J. Schoukens, An Unstructured Flexible Nonlinear Model for the Cascaded Water-Tanks Benchmark, 20th World Congress The International Federation of Automatic Control, 2017, 454-459.
  4. M. Brunot, A. Janot, F. Carrillo, Continuous-Time Nonlinear Systems Identification with Output Error Method Based on Derivative-Free Optimisation, 20th World Congress The International Federation of Automatic Control, 2017, 466-471.
  5. J. Belz, T. Münker, T.O. Heinz, G. Kampmann, O. Nelles, Automatic Modeling with Local Model Networks for Benchmark Processes, 20th World Congress The International Federation of Automatic Control, 2017, 472-477.
  6. G. Birpoutsoukis, P.Z. Csurcsia, J. Schoukens, Nonparametric Volterra Series Estimate of the Cascaded Water Tanks Using Multidimensional Regularization, 20th World Congress The International Federation of Automatic Control, 2017, 478-483.
  7. P.Z. Csurcsia, G. Birpoutsoukis and J. Schoukens, Transient elimination and memory saving possibilities for multidimensional nonparametric regularization illustrated on the cascaded water tanks benchmark problem, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  8. G. Holmes, T. Rogers, E.J. Cross, N. Dervilis, G. Manson, R.J. Barthorpe, K. Worden, Cascaded Tanks Benchmark: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  9. G. Giordano, J. Sjöberg, Cascade Tanks Benchmark, Workshop on Nonlinear System Identification Benchmarks, 2016.
  10. R. Relan, K. Tiels, A. Marconato, Identifying an Unstructured Flexible Nonlinear Model for the Cascaded Water-tanks Benchmark: Capabilities and Short-comings, Workshop on Nonlinear System Identification Benchmarks, 2016.
  11. P. Mattson, D. Zachariah, P. Stoica, Identification of a PWARX model for the cascade water tanks, Workshop on Nonlinear System Identification Benchmarks, 2016.
  12. G. Birpoutsoukis, P.Z. Csurcsia, Nonparametric Volterra series estimate of the cascaded tank, Workshop on Nonlinear System Identification Benchmarks, 2016.
  13. M. Schoukens, F.G. Scheiwe, Modeling Nonlinear Systems Using a Volterra Feedback Model, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  14. A. Svensson, F. Lindsten, T.B. Schön, First principles and black box modeling of the cascaded water tanks, Workshop on Nonlinear System Identification Benchmarks, 2016.

Wiener-Hammerstein Process Noise System (2016)

  1. M. Schoukens, J.P. Noël, Three Benchmarks Addressing Open Challenges in Nonlinear System Identification, 20th World Congress The International Federation of Automatic Control, 2017, 448-453. slides
  2. J. Belz, T. Münker, T.O. Heinz, G. Kampmann, O. Nelles, Automatic Modeling with Local Model Networks for Benchmark Processes, 20th World Congress The International Federation of Automatic Control, 2017, 472-477.
  3. T. Münker, T.O. Heinz and O. Nelles, Regularized local FIR model networks for a Bouc-Wen and a Wiener-Hammerstein system, Workshop on Nonlinear System Identification Benchmarks, 2017.
  4. E. Zhang and M. Schoukens, Fast location of process noise for nonlinear system identification, Workshop on Nonlinear System Identification Benchmarks, 2017. slides, Matlab script
    Readme in the Matlab script.
  5. G. Giordano and J. Sjöberg, Maximum likelihood identification of Wiener-Hammerstein models in presence of process noise, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  6. R. Relan, D. Verbeke and K. Tiels, One step ahead prediction of the WH benchmark with process noise using kernel adaptive learning, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  7. K. Tiels, PNLSS 1.0 - A polynomial nonlinear state-space Matlab toolbox, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  8. A. Svensson, F. Lindsten, T.B. Schön, Particle methods for the Wiener-Hammerstein system, Workshop on Nonlinear System Identification Benchmarks, 2016.
  9. E. Zhang, M. Schoukens, J. Schoukens, Structural modeling of Wiener-Hammerstein system in the presence of the process noise, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  10. M. Rébillat, K. Ege, N. Mechbal, J. Antoni, Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties, Workshop on Nonlinear System Identification Benchmarks, 2016.
  11. M. Schoukens, Identification of Wiener-Hammerstein systems with process noise using an Errors-in-Variables framework, Workshop on Nonlinear System Identification Benchmarks, 2016. slides
  12. K. Worden, G. Manson, R.J. Barthorpe, E.J. Cross, N. Dervilis, G. Holmes, T. Rogers, Wiener-Hammerstein Benchmark with process noise: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.

Bouc-Wen System (2016)

  1. M. Schoukens, J.P. Noël, Three Benchmarks Addressing Open Challenges in Nonlinear System Identification, 20th World Congress The International Federation of Automatic Control, 2017, 448-453. slides
  2. A. Fakhrizadeh Esfahani, P. Dreesen, K. Tiels, J.P. Noël, J. Schoukens, Polynomial State-Space Model Decoupling for the Identification of Hysteretic Systems, 20th World Congress The International Federation of Automatic Control, 2017, 460-465.
  3. M. Brunot, A. Janot, F. Carrillo, Continuous-Time Nonlinear Systems Identification with Output Error Method Based on Derivative-Free Optimisation, 20th World Congress The International Federation of Automatic Control, 2017, 466-471.
  4. J. Belz, T. Münker, T.O. Heinz, G. Kampmann, O. Nelles, Automatic Modeling with Local Model Networks for Benchmark Processes, 20th World Congress The International Federation of Automatic Control, 2017, 472-477.
  5. G. Hollander, P. Dreesen, M. Ishteva and J. Schoukens, Nonlinear model decoupling using a tensor decomposition initialization, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  6. T. Münker, T.O. Heinz and O. Nelles, Regularized local FIR model networks for a Bouc-Wen and a Wiener-Hammerstein system, Workshop on Nonlinear System Identification Benchmarks, 2017.
  7. M. Rébillat and M. Schoukens, A methodology to compare two estimation methods for parallel Hammerstein models, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  8. A.F. Esfahani, P. Dreesen, J.P. Noël, K. Tiels, J. Schoukens, Decoupled polynomial nonlinear state space models of a Bouc-Wen hysteretic system, Workshop on Nonlinear System Identification Benchmarks, 2017.
  9. D. Westwick, G. Hollander and J. Schoukens, The decoupled polynomial NARX model: parameter reduction and structural insights for the Bouc-Wen benchmark, Workshop on Nonlinear System Identification Benchmarks, 2017. slides
  10. J.P. Noël, A.F. Esfahani, G. Kerschen, J. Schoukens, A nonlinear state-space solution to a hysteretic benchmark in system identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  11. A.F. Esfahani, P. Dreesen, K. Tiels, J.P. Noël, J. Schoukens, Using a polynomial decoupling algorithm for state-space identification of a Bouc-Wen system, Workshop on Nonlinear System Identification Benchmarks, 2016.
  12. R. Gaasbeek, R. Mohan, Control-focused identification of hysteric systems: Selecting model structures? Think about the final use of the model!, Workshop on Nonlinear System Identification Benchmarks, 2016.
  13. A. Bajrić, System identification of a linearized hysteretic system using covariance driven stochastic subspace identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  14. M. Rébillat, K. Ege, N. Mechbal, J. Antoni, Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties, Workshop on Nonlinear System Identification Benchmarks, 2016.
  15. G. Manson, R.J. Barthorpe, E.J. Cross, N. Dervilis, G. Holmes, T. Rogers, K. Worden, Bouc-Wen Benchmark: Parametric and Nonparametric Identification, Workshop on Nonlinear System Identification Benchmarks, 2016.
  16. E. Louarroudi, S. Vanlanduit, R. Pintelon, Identification of non-linear restoring forces through linear time-periodic approximations, Workshop on Nonlinear System Identification Benchmarks, 2016.
  17. M. Schoukens, F.G. Scheiwe, Modeling Nonlinear Systems Using a Volterra Feedback Model, Workshop on Nonlinear System Identification Benchmarks, 2016. slides

Parallel Wiener-Hammerstein (2015)

  1. M. Schoukens, A. Marconato, R. Pintelon, G. Vandersteen, Y. Rolain, Parametric identification of parallel Wiener–Hammerstein systems, Automatica, 2015, 51, 111-122.
  2. M. Schoukens, K. Tiels, M. Ishteva, J. Schoukens, Identification of parallel Wiener-Hammerstein systems with a decoupled static nonlinearity, 19th World Congress The International Federation of Automatic Control, 2014, 505-510. slides
  3. P. Dreesen, M. Schoukens, K. Tiels, J. Schoukens, Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach, Instrumentation and Measurement Technology Conference (I2MTC), 2015, 987-992.

Wiener-Hammerstein System (2009)

  1. A. Svensson, T.B. Schön, A. Solin, S. Särkkä, Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion, proceedings of the 6th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), Cancun, Mexico, December 2015, 213-221.
  2. A. Naitali, F. Giri, Wiener–Hammerstein system identification – an evolutionary approach, International Journal of Systems Science, 2016, 47, 45-61.
  3. H. Ase, T. Katayama, A subspace-based identification of Wiener–Hammerstein benchmark model, Control Engineering Practice, 2015, 44, 126-137.
  4. E. de la Rosa, W. Yu, X. Li, Nonlinear system identification using deep learning and randomized algorithms, IEEE International Conference on Information and Automation, 2015, 274-279.
  5. M. Schoukens, R. Pintelon, Y. Rolain, Identification of Wiener–Hammerstein systems by a nonparametric separation of the best linear approximation, Automatica, 2014, 50, 628-634.
  6. L. Vanbeylen, A fractional approach to identify Wiener–Hammerstein systems, Automatica, 2014, 50, 903-909.
  7. A. Marconato, J. Sjöberg, J.A.K. Suykens, J. Schoukens, Improved Initialization for Nonlinear State-Space Modeling, IEEE Transactions on Instrumentation and Measurement, 2014, 63, 972-980.
  8. O. Taouali, I. Elaissi, H. Messaoud, Hybrid kernel identification method based on support vector regression and regularisation network algorithms, IET Signal Processing, 2014, 8, 981-989.
  9. A. Marconato, M. Schoukens, Y. Rolain, J. Schoukens, Study of the effective number of parameters in nonlinear identification benchmarks, IEEE 52nd Annual Conference on Decision and Control (CDC), 2013, 4308-4313. slides
  10. R. Frigola, C.E. Rasmussen, Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes, IEEE 52nd Annual Conference on Decision and Control (CDC), 2013, 5371-5376.
  11. H.M. Romero Ugalde, J.C. Carmona, V.M. Alvarado, J. Reyes-Reyes, Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters, Neurocomputing, 2013, 101, 170-180.
  12. O. Taouali, I. Elaissi, H. Messaoud, Design and comparative study of online kernel methods identification of nonlinear system in RKHS space, Artificial Intelligence Review, 2012, 37, 289-300.
  13. O. Taouali, I. Elaissi, H. Messaoud, Online identification of nonlinear system using reduced kernel principal component analysis, Neural Computing and Applications, 2012, 21, 161–169.
  14. D.T. Westwick, J. Schoukens, Initial estimates of the linear subsystems of Wiener–Hammerstein models, Automatica, 2012, 48, 2931-2936.
  15. D.T. Westwick, J. Schoukens, Classification of the Poles and Zeros of the Best Linear Approximations of Wiener-Hammerstein Systems, 16th IFAC Symposium on System Identification (SYSID), 2012, 470-475.
  16. A. Wills, B. Ninness, Generalised Hammerstein–Wiener system estimation and a benchmark application, Control Engineering Practice, 2012, 20, 1097-1108.
  17. L. Piroddi, M. Farina, M. Lovera, Black box model identification of nonlinear input–output models: A Wiener–Hammerstein benchmark, Control Engineering Practice, 2012, 20, 1109-1118.
  18. J. Sjöberg, L. Lauwers, J. Schoukens, Identification of Wiener–Hammerstein models: Two algorithms based on the best split of a linear model applied to the SYSID'09 benchmark problem, Control Engineering Practice, 2012, 20, 1119-1125.
  19. A. Marconato, J. Sjöberg, J. Schoukens, Initialization of nonlinear state-space models applied to the Wiener–Hammerstein benchmark, Control Engineering Practice, 2012, 20, 1126-1132.
  20. J. Paduart, L. Lauwers, R. Pintelon, J. Schoukens, Identification of a Wiener–Hammerstein system using the polynomial nonlinear state space approach, Control Engineering Practice, 2012, 20, 1133-1139.
  21. A.H. Tan, H.K. Wong, K. Godfrey, Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique, Control Engineering Practice, 2012, 20, 1140-1148.
  22. Y. Han, R.A. de Callafon, Identification of Wiener–Hammerstein benchmark model via rank minimization, Control Engineering Practice, 2012, 20, 1149-1155.
  23. P.L. dos Santos, J.A. Ramos, J.M. de Carvalho, Identification of a Benchmark Wiener–Hammerstein: A bilinear and Hammerstein–Bilinear model approach, Control Engineering Practice, 2012, 20, 1156-1164.
  24. T. Falck, P. Dreesen, K. De Brabanter, K. Pelckmans, B. De Moor, J.A. Suykens, Least-Squares Support Vector Machines for the identification of Wiener–Hammerstein systems, Control Engineering Practice, 2012, 20, 1165-1174.
  25. F. Giri, E.W. Bai (Editors), Block-oriented Nonlinear System Identification, Springer, 2010.
  26. A. Marconato, J. Schoukens, Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines, 15th IFAC Symposium on System Identification (SYSID), 2009, 816-819.
  27. T. Falck, K. Pelckmans, J.A. Suykens, B. De Moor, Identification of Wiener-Hammerstein Systems using LS-SVMs, 15th IFAC Symposium on System Identification (SYSID), 2009, 820-825.
  28. K. De Brabanter, P. Dreesen, P. Karsmakers, K. Pelckmans, J. De Brabanter, J. Suykens, Bart De Moor, Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark, 15th IFAC Symposium on System Identification (SYSID), 2009, 826-831.
  29. P.L. dos Santos, J.A. Ramos, J.M. de Carvalho, Identification of a Benchmark Wiener-Hammerstein System by Bilinear and Hammerstein-Bilinear Models, 15th IFAC Symposium on System Identification (SYSID), 2009, 832-837.
  30. G. Pillonetto, A. Chiuso, Gaussian Processes for Wiener-Hammerstein system identification, 15th IFAC Symposium on System Identification (SYSID), 2009, 838-843.
  31. N.V. Truong, L. Wang, Benchmark Nonlinear System Identification using Wavelet based SDP Models, 15th IFAC Symposium on System Identification (SYSID), 2009, 844-849.
  32. L. Piroddi, M. Farina, M. Lovera, Polynomial NARX Model Identification: a Wiener–Hammerstein Benchmark, 15th IFAC Symposium on System Identification (SYSID), 2009, 1074-1079.
  33. J. Paduart, L. Lauwers, R. Pintelon, J. Schoukens, Identification of a Wiener-Hammerstein System Using the Polynomial Nonlinear State Space Approach, 15th IFAC Symposium on System Identification (SYSID), 2009, 1080-1085.
  34. A. van Mulders, J. Schoukens, M. Volckaert, M. Diehl, Two Nonlinear Optimization Methods for Black Box Identification Compared, 15th IFAC Symposium on System Identification (SYSID), 2009, 1086-1091.
  35. H. Ase, T. Katayama, H. Tanaka, A State-Space Approach to Identification of Wiener-Hammerstein Benchmark Model, 15th IFAC Symposium on System Identification (SYSID), 2009, 1092-1097.
  36. L. Lauwers, R. Pintelon, J. Schoukens, Modelling of Wiener-Hammerstein Systems via the Best Linear Approximation, 15th IFAC Symposium on System Identification (SYSID), 2009, 1098-1103.
  37. A. Wills, B. Ninness, Estimation of Generalised Hammerstein-Wiener Systems, 15th IFAC Symposium on System Identification (SYSID), 2009, 1104-1109.
  38. J. Schoukens, J. Suykens, L. Ljung, Wiener-Hammerstein Benchmark, 15th IFAC Symposium on System Identification (SYSID), 2009.

Silverbox (2004)

  1. C.L.C. Mattos, G.A. Barreto, G. Acuna, Randomized Neural Networks for Recursive System Identification in the Presence of Outliers: A Performance Comparison, IWANN 2017: Advances in Computational Intelligence, 2017, 603-615.
  2. A. Carini, G.L. Sicuranza, Recursive functional link polynomial filters: An introduction, 24th European Signal Processing Conference (EUSIPCO), 2016, 2335-2339.
  3. R. Castro, S. Mehrkanoon, A. Marconato, J. Schoukens, J.A.K. Suykens, SVD truncation schemes for fixed-size kernel models, International Joint Conference on Neural Networks (IJCNN), 2014, 3922-3929.
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