HŘEBÍK, R. and J. KUKAL. Anomalous and traditional diffusion modelling in SOM learning. Archives of Control Sciences. 2019, 29(4), 699-717. ISSN 2300-2611. DOI 10.24425/acs.2019.131233.
The traditional self organizing map (SOM) is learned by Kohonen learning. The main disadvantage of this approach is in epoch based learning when the radius and rate of learning are decreasing functions of epoch index. The aim of study is to demonstrate advantages of diffusive learning in single epoch learning and other cases for both traditional and anomalous diffusion models. We also discuss the differences between traditional and anomalous learning in models and in quality of obtained SOM. The anomalous diffusion model leads to less accurate SOM which is in accordance to biological assumptions of normal diffusive processes in living nervous system. But the traditional Kohonen learning has been overperformed by novel diffusive learning approaches.
eng
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Polska akademia nauk
dc.relation.ispartof
Archives of Control Sciences
dc.subject
self organization
eng
dc.subject
Kohonen map
eng
dc.subject
diffusion learning
eng
dc.subject
anomalous diffusion
eng
dc.subject
SOM
eng
dc.title
Anomalous and traditional diffusion modelling in SOM learning