DocumentCode
1929167
Title
Trust region nonlinear optimization learning method for dynamic synapse neural networks
Author
Narnarvar, H.H. ; Berger, Theodore W.
Author_Institution
Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume
4
fYear
2003
fDate
20-24 July 2003
Firstpage
2848
Abstract
We formulate the dynamic synapse neural network from the averaged activity of local population of neurons perspective. We have applied the trust region nonlinear optimization approach to train the network and show the new learning method effectiveness in comparison to the genetic algorithms by optimizing large-scale networks.
Keywords
learning (artificial intelligence); neural nets; nonlinear programming; averaged activity; dynamic synapse neural networks; large-scale network optimization; learning method effectiveness; local population; trust region nonlinear optimization approach; trust region nonlinear optimization learning method; Artificial neural networks; Biological neural networks; Biological system modeling; Genetic algorithms; Large-scale systems; Learning systems; Neural networks; Neurons; Optimization methods; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1224023
Filename
1224023
Link To Document