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
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;
Conference_Titel :
Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean
Conference_Location :
Bari
Print_ISBN :
0-7803-3109-5
DOI :
10.1109/MELCON.1996.551294
