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Selected contributions from the workshops “Computational Intelligence” in 2023 and 2024

  • Ralf Mikut

    Prof. Dr.-Ing. Ralf Mikut is the head of the Department for Automated Image and Data Analysis at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. Research interests: Computational Intelligence; Data Analytics; Applied Artificial Intelligence for Biology, Chemistry, Medical Engineering, Manufacturing and Energy Systems.

     EMAIL logo     , 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.
Published/Copyright: October 10, 2025
at - Automatisierungstechnik
From the journal at - Automatisierungstechnik Volume 73 Issue 10

Published Online: 2025-10-10
Published in Print: 2025-10-27

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Selected contributions from the workshops “Computational Intelligence” in 2023 and 2024
  4. Methods
  5. Nonlinear system categorization for structural data mining with state space models
  6. Incorporation of structural properties of the response surface into oblique model trees
  7. Takagi-Sugeno based model reference control for wind turbine systems in frequency containment scenarios
  8. On autoregressive deep learning models for day-ahead wind power forecasts with irregular shutdowns due to redispatching
  9. Applications
  10. Efficiently determining the effect of data set size on autoencoder-based metamodels for structural design optimization
  11. Kalibriermodellerstellung und Merkmalsselektion für die mikromagnetische Materialcharakterisierung mittels maschineller Lernverfahren
  12. Investigating quality inconsistencies in the ultra-high performance concrete manufacturing process using a search-space constrained non-dominated sorting genetic algorithm II
  13. EAP4EMSIG – enhancing event-driven microscopy for microfluidic single-cell analysis
https://doi.org/10.1515/auto-2025-0085

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Selected contributions from the workshops “Computational Intelligence” in 2023 and 2024
  4. Methods
  5. Nonlinear system categorization for structural data mining with state space models
  6. Incorporation of structural properties of the response surface into oblique model trees
  7. Takagi-Sugeno based model reference control for wind turbine systems in frequency containment scenarios
  8. On autoregressive deep learning models for day-ahead wind power forecasts with irregular shutdowns due to redispatching
  9. Applications
  10. Efficiently determining the effect of data set size on autoencoder-based metamodels for structural design optimization
  11. Kalibriermodellerstellung und Merkmalsselektion für die mikromagnetische Materialcharakterisierung mittels maschineller Lernverfahren
  12. Investigating quality inconsistencies in the ultra-high performance concrete manufacturing process using a search-space constrained non-dominated sorting genetic algorithm II
  13. EAP4EMSIG – enhancing event-driven microscopy for microfluidic single-cell analysis
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