Ersü, Enis ; Tolle, Henning (2023)
A New Concept for Learning Control Inspired by Brain Theory.
In: IFAC Proceedings Volumes, 1984, 17 (2)
doi: 10.26083/tuprints-00023398
Artikel, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
The paper explains an unconventional learning control method based on assumptions in the literature about human problem solving and information storage in neuronal networks. The on-line learning comprises two stages: The dynamic input-output behaviour of the process to be controlled is stored stepwise in a neuron-like manner into an associative memory as a predictive process model, the control strategy planned via this model by optimization of a goal oriented performance index is then trained in the same way into a second associative memory. As a general mapping the learned behaviour is in both cases in general nonlinear, and by this such a control design is especially suited for strongly nonlinear processes. Simulations demonstrate the applicability of the new control concept.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Ersü, Enis ; Tolle, Henning |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A New Concept for Learning Control Inspired by Brain Theory |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 1984 |
| Verlag: | IFAC - International Federation of Automatic Control |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IFAC Proceedings Volumes |
| Jahrgang/Volume einer Zeitschrift: | 17 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.26083/tuprints-00023398 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23398 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | The paper explains an unconventional learning control method based on assumptions in the literature about human problem solving and information storage in neuronal networks. The on-line learning comprises two stages: The dynamic input-output behaviour of the process to be controlled is stored stepwise in a neuron-like manner into an associative memory as a predictive process model, the control strategy planned via this model by optimization of a goal oriented performance index is then trained in the same way into a second associative memory. As a general mapping the learned behaviour is in both cases in general nonlinear, and by this such a control design is especially suited for strongly nonlinear processes. Simulations demonstrate the applicability of the new control concept. |
| Freie Schlagworte: | Associative memory systems, adaptive control, artificial intelligence, biocyberaetics, brain models, learning systems, neuronal networks, nonlinear control |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-233980 |
| Zusätzliche Informationen: | Zugl. Konferenzveröffentlichung: 9th IFAC World Congress: A Bridge Between Control Science and Technology, 02.-06.07.1984, Budapest, Hungary |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Intelligente Systeme |
| Hinterlegungsdatum: | 28 Apr 2023 08:26 |
| Letzte Änderung: | 03 Mai 2023 10:43 |
| PPN: | |
| Zugehörige Links: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum