DocumentCode
2955597
Title
Kohonen feature map associative memory with area representation for sequential analog patterns
Author
Shiratori, Tomonori ; Osana, Yuko
Author_Institution
Tokyo Univ. of Technol., Tokyo
fYear
2008
fDate
1-8 June 2008
Firstpage
818
Lastpage
823
Abstract
In this paper, we propose a Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the Kohonen feature map associative memory with area representation for sequential patterns. Although the conventional Kohonen feature map associative memory with area representation for sequential patterns can deal with only binary (bipolar) patterns, the proposed model can deal not only binary (bipolar) patterns but also analog patterns. The proposed model can learn sequential analog patterns successively, and has robustness for damaged neurons. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed model.
Keywords
content-addressable storage; learning (artificial intelligence); self-organising feature maps; Kohonen feature map associative memory; area representation; sequential analog pattern learning; Associative memory; Hebbian theory; Information processing; Neural networks; Neurons; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location
Hong Kong
ISSN
1098-7576
Print_ISBN
978-1-4244-1820-6
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2008.4633891
Filename
4633891
Link To Document