DocumentCode
328266
Title
A global optimization algorithm for neural network training
Author
Chen, Lianhui
Author_Institution
Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Clayton, Vic., Australia
Volume
1
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
443
Abstract
The main thrust of the research is to develop a global optimization algorithm tailored for multilayer feedforward back-propagation neural network training. The goal in designing the algorithm is to tackle the problem of reaching nonoptimal network configurations due to being trapped by a saddle point or a local minimum so that continuous learning through automatic online retraining is feasible.
Keywords
backpropagation; feedforward neural nets; multilayer perceptrons; optimisation; automatic online retraining; continuous learning; global optimization algorithm; local minimum; multilayer feedforward back-propagation neural network training; nonoptimal network configurations; saddle point; Algorithm design and analysis; Australia; Computer networks; Design optimization; Feedforward neural networks; Humans; Multi-layer neural network; Neural networks; Neurons; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.713950
Filename
713950
Link To Document