HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Castillo, E. Articles by Alonso-Betanzos, A. Search for Related Content PubMed PubMed Citation Articles by Castillo, E. Articles by Alonso-Betanzos, A.

A Global Optimum Approach for One-Layer Neural Networks

castie{at}unican.es, Department of Applied Mathematics and Computational Sciences, University of Cantabria and University of Castilla-La Mancha, 39005 Santander, Spain

Oscar Fontenla-Romero

oscarfon{at}mail2.udc.es, Department of Computer Science, Faculty of Informatics, University of A Coruña, 15071 A Coruña, Spain

Bertha Guijarro-Berdiñas

cibertha{at}udc.es, Department of Computer Science, Faculty of Informatics, University of A Coruña, 15071 A Coruña, Spain

Amparo Alonso-Betanzos

ciamparo{at}udc.es, Department of Computer Science, Faculty of Informatics, University of A Coruña, 15071 A Coruña, Spain

The article presents a method for learning the weights in one-layer feedforward neural networks minimizing either the sum of squared errors or the maximum absolute error, measured in the input scale. This leads to the existence of a global optimum that can be easily obtained solving linear systems of equations or linear programming problems, using much less computational power than the one associated with the standard methods. Another version of the method allows computing a large set of estimates for the weights, providing robust, mean or median, estimates for them, and the associated standard errors, which give a good measure for the quality of the fit. Later, the standard one-layer neural network algorithms are improved by learning the neural functions instead of assuming them known. A set of examples of applications is used to illustrate the methods. Finally, a comparison with other high-performance learning algorithms shows that the proposed methods are at least 10 times faster than the fastest standard algorithm used in the comparison.

This article has been cited by other articles:

				I. Tyukin, C. van Leeuwen, and D. Prokhorov Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach Neural Comput., October 1, 2003; 15(10): 2419 - 2455. [Abstract] [Full Text] [PDF]