DocumentCode
671653
Title
A procedure for training recurrent networks
Author
Phan, Manh C. ; Beale, Mark H. ; Hagan, Martin T.
Author_Institution
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
fYear
2013
fDate
4-9 Aug. 2013
Firstpage
1
Lastpage
8
Abstract
In this paper, we introduce a new procedure for efficient training of recurrent neural networks. The new procedure uses a batch training method based on a modified version of the Levenberg-Marquardt algorithm. The information of gradients of individual sequences is used to mitigate the effect of spurious valleys in the error surface of recurrent networks. The method is tested on the modeling and control of several physical systems.
Keywords
learning (artificial intelligence); recurrent neural nets; Levenberg-Marquardt algorithm; batch training method; error surface; individual sequences gradients; physical systems; recurrent neural networks; spurious valleys; Adaptation models; Heuristic algorithms; Magnetic levitation; Neural networks; Prediction algorithms; Training; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location
Dallas, TX
ISSN
2161-4393
Print_ISBN
978-1-4673-6128-6
Type
conf
DOI
10.1109/IJCNN.2013.6706994
Filename
6706994
Link To Document