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Neural Computing and Applications

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01.02.2009 | Original Article

A fuzzy neighborhood-based training algorithm for feedforward neural networks

verfasst von: Mounir Ben Nasr, Mohamed Chtourou

Erschienen in: Neural Computing and Applications | Ausgabe 2/2009

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Abstract

In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and gradient descent method for training the weights of the output layer. The goal of this method is to assist the existing variable learning rate algorithms. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

Vorheriger Artikel Adaptive recurrent neural network control using a structure adaptation algorithm

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Titel: A fuzzy neighborhood-based training algorithm for feedforward neural networks
verfasst von: Mounir Ben Nasr
Mohamed Chtourou
Publikationsdatum: 01.02.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0165-z

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