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Training of artificial neural networks using differential evolution algorithm

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2 Author(s)
Slowik, A. ; Dept. of Electron. & Comput. Sci., Koszalin Univ. of Technol., Koszalin ; Bialko, M.

In the paper an application of differential evolution algorithm to training of artificial neural networks is presented. The adaptive selection of control parameters has been introduced in the algorithm; due to this property only one parameter is set at the start of proposed algorithm. The artificial neural networks to classification of parity-p problem have been trained using proposed algorithm. Results obtained using proposed algorithm have been compared to the results obtained using other evolutionary method, and gradient training methods such as: error back-propagation, and Levenberg-Marquardt method. It has been shown in this paper that application of differential evolution algorithm to artificial neural networks training can be an alternative to other training methods.

Published in:

Human System Interactions, 2008 Conference on

Date of Conference:

25-27 May 2008

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