DocumentCode
2624135
Title
A new architecture of neural network
Author
Zhang, Yongjun ; Chen, Zongzhi
Author_Institution
Inst. of Electron., Acad. Sinica, Beijing, China
fYear
1991
fDate
18-21 Nov 1991
Firstpage
833
Abstract
Describes a novel neural-network architecture, the neural network loop (NNL), and its learning rules. It can operate as Hopfield, BAM (bidirectional associative memory), and other kinds of neural networks. In particular, it can perform multiple category associative memory. This capability is very similar to that of the human brain. It can be applied to pattern recognition and associative memory. Computer simulation was carried out, and the results prove that NNL is an effective network
Keywords
content-addressable storage; learning systems; neural nets; pattern recognition; Hopfield; NNL; architecture; bidirectional associative memory; learning rules; multiple category associative memory; neural network; neural network loop; pattern recognition; Associative memory; Biological neural networks; Brain modeling; Computer simulation; Humans; Nervous system; Neural networks; Neurons; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170504
Filename
170504
Link To Document