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A New Concept for Learning Control Inspired by Brain Theory

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
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: A New Concept for Learning Control Inspired by Brain Theory
Language: English
Date: 2023
Place of Publication: Darmstadt
Year of primary publication: 1984
Publisher: IFAC - International Federation of Automatic Control
Journal or Publication Title: IFAC Proceedings Volumes
Volume of the journal: 17
Issue Number: 2
DOI: 10.26083/tuprints-00023398
Corresponding Links:
Origin: Secondary publication service

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.

Uncontrolled Keywords: Associative memory systems, adaptive control, artificial intelligence, biocyberaetics, brain models, learning systems, neuronal networks, nonlinear control
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-233980
Additional Information:

Zugl. Konferenzveröffentlichung: 9th IFAC World Congress: A Bridge Between Control Science and Technology, 02.-06.07.1984, Budapest, Hungary

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Intelligent Systems
Date Deposited: 28 Apr 2023 08:26
Last Modified: 06 Jul 2023 11:34
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23398
PPN: 509301266
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