DocumentCode
1378861
Title
A fast fixed point learning method to implement associative memory on CNNs
Author
Szolgay, Peter ; Szatmári, István ; László, Károly
Author_Institution
Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
Volume
44
Issue
4
fYear
1997
fDate
4/1/1997 12:00:00 AM
Firstpage
362
Lastpage
366
Abstract
Cellular Neural Networks (CNNs) with space-varying interconnections are considered here to implement associative memories. A fast learning method is presented to compute the interconnection weights. The algorithm was carefully tested and compared to other methods. Storage capacity, noise immunity, and spurious state avoidance capability of the proposed system are discussed
Keywords
cellular neural nets; character recognition; content-addressable storage; digital arithmetic; learning (artificial intelligence); Chinese character recognition; algorithm; associative memory; cellular neural networks; fast fixed point learning method; interconnection weights; noise immunity; space-varying interconnections; spurious state avoidance capability; storage capacity; Associative memory; Cellular networks; Cellular neural networks; Cloning; Error correction; Error correction codes; Learning systems; Multidimensional systems; Testing; Turing machines;
fLanguage
English
Journal_Title
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
Publisher
ieee
ISSN
1057-7122
Type
jour
DOI
10.1109/81.563627
Filename
563627
Link To Document