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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007605
