DocumentCode
303812
Title
Modelling fatigue and dynamic learning in a self-organizing neural cell model
Author
Acciani, G. ; Chiarantoni, E. ; Minenna, M.
Author_Institution
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Italy
Volume
2
fYear
1996
fDate
13-16 May 1996
Firstpage
609
Abstract
In this paper some considerations are developed to design a neural unit that takes into account a number of biological effects, namely a fluctuating threshold for the activation of the unit and a learning law dependent on the past history of the unit. The properties of this new neural unit are examined and it is shown how this unit is able to find autonomously (i.e. without requiring any interaction with other units) a local maximum of density in the input data set space
Keywords
learning (artificial intelligence); self-organising feature maps; biological effects; dynamic learning; fatigue; fluctuating activation threshold; input data set space; local maximum; self-organizing neural cell model; Artificial neural networks; Biological system modeling; Cells (biology); Equations; Fatigue; History; Information processing; Lakes; Stress;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean
Conference_Location
Bari
Print_ISBN
0-7803-3109-5
Type
conf
DOI
10.1109/MELCON.1996.551294
Filename
551294
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