DocumentCode
2616942
Title
The neural network self-healing process by using a reconstructed sample space
Author
Hodges, Russel E. ; Wu, Chwan-Hwa
Author_Institution
Dept. of Electr. Eng., Auburn Univ., AL, USA
fYear
1990
fDate
1-3 May 1990
Firstpage
204
Abstract
The inherent ability of neural networks to recover information when neurons in the network are damaged is discussed. This self-healing property is shown to exist in networks used for pattern recognition. The self-organizing feature map (SOFM) is the network used to study this topic. The SOFM has an efficient data compression technique that allows signal processing techniques to be used in recovering lost information from destroyed nodes
Keywords
data compression; neural nets; pattern recognition; data compression technique; destroyed nodes; neural network; pattern recognition; reconstructed sample space; self-healing process; self-organizing feature map; signal processing techniques; Artificial neural networks; Biological system modeling; Biomedical signal processing; Data compression; Density functional theory; Density measurement; Fault tolerant systems; Neural networks; Neurons; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location
New Orleans, LA
Type
conf
DOI
10.1109/ISCAS.1990.111972
Filename
111972
Link To Document