Title :
Who is afraid of the big bad ANN?
Author_Institution :
Licensing & Safety Div., Israeli Atomic Energy Comm., Beer-Sheva, Israel
fDate :
6/24/1905 12:00:00 AM
Abstract :
The author\´s ten-year experience with training large-scale ANN models with the PCA-CG algorithm that generates a non-random initial connection weight set is presented. The suggested small number of hidden neurons and automatic identification and removal of the less relevant inputs increases the robustness of these models. Examples of ANN modeling of "artificial nose" sensor array, TV program rating and e-mail letter importance classification demonstrate the algorithm efficiency
Keywords :
classification; intelligent sensors; learning (artificial intelligence); neural nets; principal component analysis; PCA CG algorithm; TV program rating prediction; artificial nose sensor array; e-mail letter importance classification; hidden neurons; large-scale neural networks; learning process; local minima; nonrandom initial connection weight; principal component analysis; training; Artificial neural networks; Chemical technology; Industrial training; Large-scale systems; Licenses; Neurons; Robustness; Safety; Sensor arrays; Speech analysis;
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.2002.1007444
