DocumentCode
1917980
Title
Global optimization for fast multilayer perceptron training
Author
Lee, Jaewook
Author_Institution
Dept. of Ind. Eng., Pohang Sci. & Technol. Univ., South Korea
Volume
1
fYear
2003
fDate
20-24 July 2003
Firstpage
410
Abstract
A new training algorithm for multilayer perceptrons (MLPs) is proposed. The proposed algorithm consists of two phases: a trust region-based local search for fast training of networks and a quotient-based global search for escaping local minima and moving toward a weight vector of next descent. These two phases are repeated alternatively in the weight space to achieve a goal training error. Benchmark results demonstrate a significant performance improvement of the proposed training algorithm compared with other existing training algorithms.
Keywords
learning (artificial intelligence); multilayer perceptrons; optimisation; search problems; global optimization; goal training error; multilayer perceptron training; quotient-based global search; training algorithm; trust region-based local search; weight vector; Benchmark testing; Convergence; Gradient methods; Industrial engineering; Industrial training; Multilayer perceptrons; Optimization methods; Pattern recognition; Robotics and automation; Supervised learning;
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.2003.1223381
Filename
1223381
Link To Document