DocumentCode
3308922
Title
An algorithm for fast convergence in training neural networks
Author
Wilamowski, Bogdan M. ; Iplikci, Serdar ; Kaynak, Okyay ; Efe, M. Onder
Author_Institution
Graduate Center, Idaho Univ., Boise, ID, USA
Volume
3
fYear
2001
fDate
2001
Firstpage
1778
Abstract
In this work, two modifications on Levenberg-Marquardt (LM) algorithm for feedforward neural networks are studied. One modification is made on performance index, while the other one is on calculating gradient information. The modified algorithm gives a better convergence rate compared to the standard LM method and is less computationally intensive and requires less memory. The performance of the algorithm has been checked on several example problems
Keywords
Jacobian matrices; convergence; feedforward neural nets; learning (artificial intelligence); performance index; Jacobian matrix; Levenberg-Marquardt algorithm; convergence rate; feedforward neural networks; gradient information; learning; performance index; Backpropagation algorithms; Convergence; Equations; Feedforward neural networks; Intelligent networks; Jacobian matrices; Neural networks; Newton method; Performance analysis; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938431
Filename
938431
Link To Document