PUBLIKATIONSSERVER

Martin Kohlhase

https://orcid.org/0009-0002-9374-0720

28 Publikationen

Alle markieren

[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789
Dockhorn F-M, Kohlhase M. An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing; 2024:53-76. doi:10.5445/KSP/1000174544
HSBI-PUB | DOI
 
[27]
2024 | Artikel | FH-PUB-ID: 5497
Weller J, Migenda N, Enzberg S von, Kohlhase M, Schenck W, Dumitrescu R. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[26]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus M, Migenda N, Weller J, Schenck W, Kohlhase M. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[25]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[24]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi:10.3390/math12172663
HSBI-PUB | DOI
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller J, Migenda N, Wegel A, Kohlhase M, Schenck W, Dumitrescu R. Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In: IEEE, ed. 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[22]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters J, Schöne M, Kohlhase M. Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[21]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Jaster B, Kohlhase M. Active Learning for Regression Problems with Ensemble Methods. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 33. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT); 2023:9-29. doi:10.5445/KSP/1000162754
HSBI-PUB | DOI | Download (ext.)
 
[20]
2023 | Artikel | FH-PUB-ID: 2855 | OA
Vollenkemper L, Mönikes M, Wortmann F, et al. HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems. ArbeitsweltPlus Working Paper. 2023. doi:10.55594/UXIT4205
HSBI-PUB | DOI | Download (ext.)
 
[19]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper L, Grumbach F, Kohlhase M, Reusch P. Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. wt Werkstattstechnik online. 2023;113(04):158-163. doi:10.37544/1436-4980-2023-04-58
HSBI-PUB | DOI | Download (ext.)
 
[18]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde K, Vollenkemper L, Schwan C, Kohlhase M, Schenck W. Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors. 2022;22(7). doi:10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[17]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In: Yin H, Camacho D, Tino P, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022:379-390. doi:10.1007/978-3-031-21753-1_37
HSBI-PUB | DOI
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
Hanitz M, Schöne M, Voigt T, Kohlhase M. Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In: Perner P, ed. Machine Learning and Data Mining in Pattern Recognition, MLDM 2022. Leipzig: ibai-publishing; 2022:121-135.
HSBI-PUB | Download (ext.)
 
[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
Vollenkemper L, Kohlhase M. Spatial Temporal Transformer Networks for Sparse Motion Capture Applications. In: Schulte H, Hoffman F, Mikut R, Karlsruhier Institut für Technologie (KIT), eds. PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE. Vol 32. WORKSHOP COMPUTATIONAL INTELLIGENCE. Karlsruhe: KIT Scientific Publishing; 2022. doi:10.5445/KSP/1000151141
HSBI-PUB | DOI | Download (ext.)
 
[14]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne M, Kohlhase M. Curvature-Oriented Splitting for Multivariate Model Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2021:01-09. doi:10.1109/SSCI50451.2021.9659858
HSBI-PUB | DOI | Download (ext.)
 
[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
Ewerszumrode J, Schöne M, Godt S, Kohlhase M. Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 31. Workshop Computational Intelligence . KIT Scientific Publishing; 2021:285-305. doi:10.5445/KSP/1000138532
HSBI-PUB | DOI | Download (ext.)
 
[12]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Space-Filling Designs for Experiments with Assembled Products. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:192-199. doi:10.1145/3460824.3460854
HSBI-PUB | DOI | Download (ext.)
 
[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
Voigt T, Kohlhase M, Nelles O. Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics. 2021;9(19). doi:10.3390/math9192479
HSBI-PUB | DOI | Download (ext.)
 
[10]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[9]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann L, Migenda N, Voigt T, Kohlhase M, Schenck W. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[8]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne M, Kohlhase M. Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide C, Novais P, Camacho D, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020:41-50. doi:10.1007/978-3-030-62362-3_5
HSBI-PUB | DOI | Download (ext.)
 
[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
Schöne M, Kohlhase M. Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI | Download (ext.)
 
[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
Godt S, Kohlhase M. Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020:159-180. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt T, Kohlhase M, Nelles O. Incremental Latin Hypercube Additive Design for LOLIMOT. In: Institute of Electrical and Electronics Engineers (IEEE), ed. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1602-1609. doi:10.1109/ETFA46521.2020.9212173
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
Voigt T, Kohlhase M, Peter A. Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin. 2020;62(04):62-69.
HSBI-PUB
 
[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
Voigt T, Kohlhase M, Nelles O. Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In: Proceedings - 29. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe; 2019:267-288. doi:10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
Godt S, Kohlhase M. Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2019:51-72. doi:10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
Voigt T, Kohlhase M. Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In: Proceedings - 28. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe; 2018:93-111.
HSBI-PUB | Download (ext.)
 
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28 Publikationen

Alle markieren

[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789
Dockhorn F-M, Kohlhase M. An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing; 2024:53-76. doi:10.5445/KSP/1000174544
HSBI-PUB | DOI
 
[27]
2024 | Artikel | FH-PUB-ID: 5497
Weller J, Migenda N, Enzberg S von, Kohlhase M, Schenck W, Dumitrescu R. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[26]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus M, Migenda N, Weller J, Schenck W, Kohlhase M. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[25]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[24]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi:10.3390/math12172663
HSBI-PUB | DOI
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller J, Migenda N, Wegel A, Kohlhase M, Schenck W, Dumitrescu R. Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In: IEEE, ed. 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[22]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters J, Schöne M, Kohlhase M. Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[21]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Jaster B, Kohlhase M. Active Learning for Regression Problems with Ensemble Methods. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 33. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT); 2023:9-29. doi:10.5445/KSP/1000162754
HSBI-PUB | DOI | Download (ext.)
 
[20]
2023 | Artikel | FH-PUB-ID: 2855 | OA
Vollenkemper L, Mönikes M, Wortmann F, et al. HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems. ArbeitsweltPlus Working Paper. 2023. doi:10.55594/UXIT4205
HSBI-PUB | DOI | Download (ext.)
 
[19]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper L, Grumbach F, Kohlhase M, Reusch P. Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. wt Werkstattstechnik online. 2023;113(04):158-163. doi:10.37544/1436-4980-2023-04-58
HSBI-PUB | DOI | Download (ext.)
 
[18]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde K, Vollenkemper L, Schwan C, Kohlhase M, Schenck W. Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors. 2022;22(7). doi:10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[17]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In: Yin H, Camacho D, Tino P, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022:379-390. doi:10.1007/978-3-031-21753-1_37
HSBI-PUB | DOI
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
Hanitz M, Schöne M, Voigt T, Kohlhase M. Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In: Perner P, ed. Machine Learning and Data Mining in Pattern Recognition, MLDM 2022. Leipzig: ibai-publishing; 2022:121-135.
HSBI-PUB | Download (ext.)
 
[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
Vollenkemper L, Kohlhase M. Spatial Temporal Transformer Networks for Sparse Motion Capture Applications. In: Schulte H, Hoffman F, Mikut R, Karlsruhier Institut für Technologie (KIT), eds. PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE. Vol 32. WORKSHOP COMPUTATIONAL INTELLIGENCE. Karlsruhe: KIT Scientific Publishing; 2022. doi:10.5445/KSP/1000151141
HSBI-PUB | DOI | Download (ext.)
 
[14]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne M, Kohlhase M. Curvature-Oriented Splitting for Multivariate Model Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2021:01-09. doi:10.1109/SSCI50451.2021.9659858
HSBI-PUB | DOI | Download (ext.)
 
[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
Ewerszumrode J, Schöne M, Godt S, Kohlhase M. Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 31. Workshop Computational Intelligence . KIT Scientific Publishing; 2021:285-305. doi:10.5445/KSP/1000138532
HSBI-PUB | DOI | Download (ext.)
 
[12]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Space-Filling Designs for Experiments with Assembled Products. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:192-199. doi:10.1145/3460824.3460854
HSBI-PUB | DOI | Download (ext.)
 
[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
Voigt T, Kohlhase M, Nelles O. Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics. 2021;9(19). doi:10.3390/math9192479
HSBI-PUB | DOI | Download (ext.)
 
[10]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[9]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann L, Migenda N, Voigt T, Kohlhase M, Schenck W. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[8]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne M, Kohlhase M. Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide C, Novais P, Camacho D, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020:41-50. doi:10.1007/978-3-030-62362-3_5
HSBI-PUB | DOI | Download (ext.)
 
[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
Schöne M, Kohlhase M. Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI | Download (ext.)
 
[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
Godt S, Kohlhase M. Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020:159-180. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt T, Kohlhase M, Nelles O. Incremental Latin Hypercube Additive Design for LOLIMOT. In: Institute of Electrical and Electronics Engineers (IEEE), ed. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1602-1609. doi:10.1109/ETFA46521.2020.9212173
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
Voigt T, Kohlhase M, Peter A. Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin. 2020;62(04):62-69.
HSBI-PUB
 
[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
Voigt T, Kohlhase M, Nelles O. Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In: Proceedings - 29. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe; 2019:267-288. doi:10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
Godt S, Kohlhase M. Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2019:51-72. doi:10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
Voigt T, Kohlhase M. Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In: Proceedings - 28. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe; 2018:93-111.
HSBI-PUB | Download (ext.)
 
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