DocumentCode
1681476
Title
Implicit learning in autoencoder novelty assessment
Author
Thompson, Benjamin B. ; Marks, Robert J., II ; Choi, Jai J. ; El-Sharkawi, Mohamed A. ; Huang, Ming-Yuh ; Bunje, Carl
Author_Institution
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
2878
Lastpage
2883
Abstract
When the situation arises that only "normal" behavior is known about a system, it is desirable to develop a system based solely on that behavior which enables the user to determine when that system behavior falls outside of that range of normality. A new method is proposed for detecting such novel behavior through the use of autoassociative neural network encoders, which can be shown to implicitly learn the nature of the underlying "normal" system behavior
Keywords
encoding; learning (artificial intelligence); neural nets; autoassociative neural network encoders; autoencoder novelty assessment; implicit learning; Chaos; Computational intelligence; Fault detection; Feedforward neural networks; Feedforward systems; Gaussian noise; Imaging phantoms; Laboratories; Monitoring; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007605
Filename
1007605
Link To Document