Supervised training algorithms for B-spline neural networks and fuzzy systems

A.E Ruano,J.V Oliveira,C Cabrita,L.T Koczy,D Tikk

doi:10.1109/nafips.2001.943675

Supervised training algorithms for B-spline neural networks and fuzzy systems

A.E Ruano, J.V Oliveira + Show 3 more

https://doi.org/10.1109/nafips.2001.943675

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Publication Date: Jul 25, 2001

Citations: 16

Affiliation: University of Algarve, Andrássy University Budapest

#Standard Error-back Propagation Algorithm #Fuzzy Systems + Show 8 more

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Abstract

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By introducing the relationships between B-spline neural networks and Mamdani (satisfying certain assumptions) fuzzy model, training algorithms developed initially for neural networks can be adapted to fuzzy systems. The standard training criterion is reformulated, by separating the linear and nonlinear parameters. By employing this reformulated criterion with the Levenberg-Marquardt algorithm, a new training method, offering a fast rate of convergence is obtained. It is also shown that the standard error-back propagation algorithm, the most common training method for this class of systems, exhibits a very poor performance.

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