DocumentCode
2707516
Title
Levenberg-Marquardt training for modular networks
Author
Fun, Meng-Hock ; Hagan, Martin T.
Author_Institution
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
Volume
1
fYear
1996
fDate
3-6 Jun 1996
Firstpage
468
Abstract
The modular neural network has been shown to be an effective alternative to multilayer feedforward networks, especially for implementing functions with sharp changes. This paper describes a new method for training modular networks, based on the Levenberg-Marquardt algorithm for nonlinear least squares. The algorithm is tested on several function approximation problems, and the performance is compared with standard steepest ascent and the Rprop algorithm
Keywords
Hessian matrices; function approximation; learning (artificial intelligence); least squares approximations; neural nets; optimisation; performance index; Hessian matrix; Levenberg-Marquardt algorithm; function approximation; learning; modular neural network; nonlinear least squares; optimisation; performance index; Approximation algorithms; Function approximation; Least squares approximation; Least squares methods; Multi-layer neural network; Neural networks; Nonhomogeneous media; Performance analysis; Testing; Transfer functions;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.548938
Filename
548938
Link To Document