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Licensed Unlicensed Requires Authentication Published by De Gruyter (O) October 17, 2023

Selected contributions from the Workshop “Computational Intelligence”

  • Ralf Mikut

    Prof. Dr.-Ing. Ralf Mikut is head of the Department for Automated Image and Data Analysis at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology and is the spokesman of the Helmholtz Information and Data Science School for Health (HIDSS4Health). Research interests: Computational Intelligence; Data Analytics; Modeling and Image Analysis with applications in biology, chemistry, medical engineering, energy systems and robotics.

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    , Andreas Kroll

    Univ.-Prof. Dr.-Ing. Andreas Kroll is head of the Department of Measurement and Control Engineering at the University of Kassel. Main research interests: Nonlinear system identification and control methods; computational intelligence; remote sensing and sensor data fusion.

    and Horst Schulte

    Prof. Dr.-Ing. Horst Schulte is head of the Control Engineering Group, Department of Engineering I at the HTW Berlin, Chairman of the Federation of German Windpower and Other Renewable Energies (FGW e.V.); Main research interests: Nonlinear and Linear Control Methods, Numerical optimization methods, Computational Intelligence in Control, Fault-tolerant Control, Power Systems and Smart Grids with Wind and PV Solar Power Plant integration.


Corresponding author: Ralf Mikut, Karlsruhe Institute of Technology (KIT), Institute for Automation and Applied Informatics, Hermann-von-Helmholtz-Platz 1, 76344 Karlsruhe, Germany, E-mail:

About the authors

Ralf Mikut

Prof. Dr.-Ing. Ralf Mikut is head of the Department for Automated Image and Data Analysis at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology and is the spokesman of the Helmholtz Information and Data Science School for Health (HIDSS4Health). Research interests: Computational Intelligence; Data Analytics; Modeling and Image Analysis with applications in biology, chemistry, medical engineering, energy systems and robotics.

Andreas Kroll

Univ.-Prof. Dr.-Ing. Andreas Kroll is head of the Department of Measurement and Control Engineering at the University of Kassel. Main research interests: Nonlinear system identification and control methods; computational intelligence; remote sensing and sensor data fusion.

Horst Schulte

Prof. Dr.-Ing. Horst Schulte is head of the Control Engineering Group, Department of Engineering I at the HTW Berlin, Chairman of the Federation of German Windpower and Other Renewable Energies (FGW e.V.); Main research interests: Nonlinear and Linear Control Methods, Numerical optimization methods, Computational Intelligence in Control, Fault-tolerant Control, Power Systems and Smart Grids with Wind and PV Solar Power Plant integration.

References

[1] N.N., GMA-Fachausschuss 5.14 Computational Intelligence [Online], 2023. Available at: https://rst.etit.tu-dortmund.de/veranstaltungen-1/gma-fachausschuss, https://rst.etit.tu-dortmund.de/veranstaltungen-1/gma-fachausschuss.Search in Google Scholar

[2] H. Schulte, F. Hoffmann, and R. Mikut, Eds., Proceedings 31. Workshop Computational Intelligence, KIT Scientific Publishing, 2021.Search in Google Scholar

[3] H. Schulte, F. Hoffmann, and R. Mikut, Eds., Proceedings 32. Workshop Computational Intelligence, KIT Scientific Publishing, 2022.Search in Google Scholar

[4] J. Timmermann, “Data-driven methods in control engineering,” in 31. Workshop Computational Intelligence, Berlin (Invited Talk), 2021.Search in Google Scholar

[5] F. A. Oliehoek, “Reinforcement learning: state of the art results and challenges,” in 31. Workshop Computational Intelligence, Berlin (Invited Talk), 2021.Search in Google Scholar

[6] Y. Wang, C. Pylatiuk, R. Mikut, R. Peravali, and M. Reischl, “Quantification platform for touch response of zebrafish larvae using machine learning,” in Proc., 31. Workshop Computational Intelligence, Berlin, KIT Scientific Publishing, 2021, pp. 37–54.Search in Google Scholar

[7] A. Cavaterra, M. Wattenberg, U. Schwalbe, and S. Lambeck, “Approximative Modellierung eines LLC-Resonanzwandlers mit Takagi-Sugeno-Modellen,” in Proc., 31. Workshop Computational Intelligence, Berlin, KIT Scientific Publishing, 2021, pp. 189–196.Search in Google Scholar

[8] L. Kistner and A. Kroll, “Systemidentifikation und Simulation nichtlinearer dynamischer Systeme mit Gaußschen Prozessmodellen mit näherungsweiser Rückführung normalverteilter Ausgangsgrößen,” in Proc., 31. Workshop Computational Intelligence, Berlin, KIT Scientific Publishing, 2021, pp. 131–148.Search in Google Scholar

[9] W. Samek, “XAI 2.0: moving from explanations to understanding,” in 32. Workshop Computational Intelligence, Berlin (Invited Talk), 2022.Search in Google Scholar

[10] J. Fähndrich, “Marker Passing als Abstraktion kunstlicher neuronaler Netze,” in 32. Workshop Computational Intelligence, Berlin (Invited Talk), 2022.Search in Google Scholar

[11] C. Diehl, T. Osterburg, N. Murzyn, G. Schneider, F. Hoffmann, and T. Bertram, “Conditional behavior prediction for automated driving on highways,” in Proc., 32. Workshop Computational Intelligence, Berlin, KIT Scientific Publishing, 2022, pp. 125–132.Search in Google Scholar

[12] F. Schneider, M. Schüssler, R. Hellmig, and O. Nelles, “Constrained design of experiments for data-driven models,” in Proc., 32. Workshop Computational Intelligence, Berlin, KIT Scientific Publishing, 2022, pp. 193–212.Search in Google Scholar

Published Online: 2023-10-17
Published in Print: 2023-10-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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