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A fuzzy neighborhood-based training algorithm for feedforward neural networks

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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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Research Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), ENIS, BP W, 3038, Sfax, Tunisia

    Mounir Ben Nasr & Mohamed Chtourou

Authors
  1. Mounir Ben Nasr
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  2. Mohamed Chtourou
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Correspondence to Mohamed Chtourou.

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Nasr, M.B., Chtourou, M. A fuzzy neighborhood-based training algorithm for feedforward neural networks. Neural Comput & Applic 18, 127–133 (2009). https://doi.org/10.1007/s00521-007-0165-z

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  • DOI: https://doi.org/10.1007/s00521-007-0165-z

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