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Convergence Properties of Associative Memory Storage for Learning Control Systems

Parks, P.C. ; Militzer, Jürgen (1989)
Convergence Properties of Associative Memory Storage for Learning Control Systems.
In: IFAC Proceedings Volumes, 23 (1)
doi: 10.1016/S1474-6670(17)52750-0
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

First, the cerebellar model articulation controller (CMAC), invented in the early 1970s by J S Albus, and the associative memory system (AMS), developed for learning control systems by H Tolle, E Ersü and J Militzer in the early 1980s, are briefly described. The underlying mathematics of the AMS learning or training algorithm is then given with a geometrical interpretation from which its convergence properties may be deduced. These are illustrated for some simple cases.

The original algorithm devised by Albus is very simple to compute but is slow to converge, and the second part of the paper investigates various methods of speeding up the algorithm. From an application of these new algorithms to test cases one is strongly recommended for further evaluation.

The results reported here are of relevance also to the topical and rapidly growing field of neural computing.

Typ des Eintrags: Artikel
Erschienen: 1989
Autor(en): Parks, P.C. ; Militzer, Jürgen
Art des Eintrags: Bibliographie
Titel: Convergence Properties of Associative Memory Storage for Learning Control Systems
Sprache: Englisch
Publikationsjahr: 1989
Ort: Darmstadt
Verlag: IFAC - International Federation of Automatic Control
Titel der Zeitschrift, Zeitung oder Schriftenreihe: IFAC Proceedings Volumes
Jahrgang/Volume einer Zeitschrift: 23
(Heft-)Nummer: 1
DOI: 10.1016/S1474-6670(17)52750-0
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Kurzbeschreibung (Abstract):

First, the cerebellar model articulation controller (CMAC), invented in the early 1970s by J S Albus, and the associative memory system (AMS), developed for learning control systems by H Tolle, E Ersü and J Militzer in the early 1980s, are briefly described. The underlying mathematics of the AMS learning or training algorithm is then given with a geometrical interpretation from which its convergence properties may be deduced. These are illustrated for some simple cases.

The original algorithm devised by Albus is very simple to compute but is slow to converge, and the second part of the paper investigates various methods of speeding up the algorithm. From an application of these new algorithms to test cases one is strongly recommended for further evaluation.

The results reported here are of relevance also to the topical and rapidly growing field of neural computing.

Zusätzliche Informationen:

Zugl. Konferenzveröffentlichung: 3rd IFAC Symposium on Adaptive Systems in control and signal Processing, 19.-21.04.1989, Glasgow, UK

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: 02 Aug 2024 12:51
Letzte Änderung: 02 Aug 2024 12:51
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