Title :
A fuzzy-controlled delta-bar-delta learning rule
Author :
Lippe, W.-M. ; Feuring, Th ; Tenhagen, A.
Author_Institution :
Inst. fur Num. Math./Inf., Westfalischen Wilhelms-Univ., Munster, Germany
fDate :
27 Jun-2 Jul 1994
Abstract :
In classic backpropagation nets, as introduced by Rumelhart et al. (1986), the weights are modified according to the method of steepest descent. The goal of this weight modification is to minimise the error in net-outputs for a given training set. Basing upon Jacobs´ work (1988), we point out drawbacks of steepest descent and suggest improvements on it. These yield a backpropagation net, which adjusts its weights according to a parallel coordinate descent method, whose parameters are being fuzzy-controlled
Keywords :
backpropagation; fuzzy control; fuzzy neural nets; multilayer perceptrons; backpropagation nets; error minimisation; fuzzy parameter control; fuzzy-controlled delta-bar-delta learning rule; parallel coordinate descent method; steepest descent; weight modification; Approximation algorithms; Backpropagation algorithms; Convergence; Fuzzy control; Jacobian matrices; Neural networks; Newton method; Rough surfaces; Surface roughness; Testing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374410
