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Supervised training algorithms for B-Spline neural networks and neuro-fuzzy systems
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Resumo(s)
Complete supervised training algorithms for B-Spline neural networks and fuzzy rulebased
systems are discussed. By introducing the relationships between B-Spline neural
networks and Mamdani (satisfying certain assumptions) and Takagi±Kang±Sugeno
fuzzy models, training algorithms developed initially for neural networks can be
adapted to fuzzy systems. The standard training criterion is reformulated, by separating its 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 and unreliable performance.
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Contexto Educativo
Citação
Ruano, A. E.; Cabrita, C.; Oliveira, J. V.; Kóczy, L. T. Supervised training algorithms for B-Spline neural networks and neuro-fuzzy systems, International Journal of Systems Science, 33, 8, 689-711, 2002.