DocumentCode :
3207728
Title :
Improved generalization learning with sliding mode control and the Levenberg-Marquadt algorithm
Author :
Costa, Marcelo Azevedo ; Braga, Antônio Pádua ; De Menezes, Benjamin Rodrigues
Author_Institution :
Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
fYear :
2002
fDate :
2002
Firstpage :
44
Lastpage :
48
Abstract :
A variation of the well known Levenberg-Marquardt for training neural networks is presented in this work. The algorithm presented restricts the norm of the weights vector to a preestablished norm value and finds the minimum error solution for that norm value. A range of different norm solutions is generated and the best generalization solution is selected. The results show the efficiency of the algorithm in terms of convergence speed and generalization performance.
Keywords :
convergence; generalisation (artificial intelligence); learning (artificial intelligence); neural nets; variable structure systems; Levenberg-Marquadt algorithm; convergence speed; generalization learning; generalization performance; minimum error solution; neural network training; sliding mode control; weight vector norm; Approximation algorithms; Computational complexity; Convergence; Equations; Error correction; Minimization methods; Neural networks; Optimization methods; Pareto optimization; Sliding mode control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on
Print_ISBN :
0-7695-1709-9
Type :
conf
DOI :
10.1109/SBRN.2002.1181433
Filename :
1181433
Link To Document :
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