Auto-adaptive neural network tree structure based on complexity estimator

Authors:
Mariusz Rybnik;Abdennasser Chebira;Kurosh Madani
Affiliations:
Laboratoire d'Etudes et de Recherches en Instrumentation, Université Paris XII, I.U.T. De Creteil-Senart, Lieusaint, France;Laboratoire d'Etudes et de Recherches en Instrumentation, Université Paris XII, I.U.T. De Creteil-Senart, Lieusaint, France;Laboratoire d'Etudes et de Recherches en Instrumentation, Université Paris XII, I.U.T. De Creteil-Senart, Lieusaint, France
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 6
Cited 1

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Abstract

This paper studies the convergence properties of the previously proposed CFA (Clustering for Function Approximation) algorithm and compares its behavior with other input-output clustering techniques also designed for approximation problems. The results obtained show that CFA is able to obtain an initial configuration from which an approximator can improve its performance.