Abstract
In this paper, we analyze characteristics of GA-based learning method of Binary Neural Networks (BNN). First, we consider coding methods to a chromosome in a GA and discuss the necessary chromosome length for a learning of BNN. Then, we compare some selection methods in a GA. We show that the learning results can be obtained in the less number of generations by properly setting selection methods and parameters in a GA. We also show that the quality of the learning results can be almost the same as that of the conventional method. These results can be verified by numerical experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gray, D.L., Michel, A.N.: A training algorithm for binary feed forward neural networks. IEEE Trans. Neural Networks 3(2), 176–194 (1992)
Kim, J.H., Park, S.K.: The geometrical learning of binary neural networks. IEEE Trans. Neural Networks 6(1), 237–247 (1995)
Windeatt, T., Tebbs, R.: Spectral technique for hidden layer neural network training. Pattern Recognition Letters 18(8), 723–731 (1997)
Gazula, S., Kabuka, M.R.: Design of supervised classifiers using Boolean neural networks. IEEE Trans. Pattern Anal. & Mach. Intell. 17(2), 1239–1246 (1995)
Wang, X.-A., Wicker, S.B.: An artificial neural net Viterbi decoder. IEEE Trans. Commun 44(2), 165–171 (1996)
Shimada, M., Saito, T.: A GA-Based Learning Algorithm for Binary Neural Networks. IEICE Trans. Funds. E85-A (11), 2544–2546 (2002)
Davis, L.: Handbook of genetic algorithms. Van Nostrand Reinhold, New York (1991)
Kawanishi, H., Hagiwara, M.: Improved Genetic Algorithms using Inverse-Elitism. T. IEE Japan 118-C(5), 707–713 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hirane, T., Toryu, T., Nakano, H., Miyauchi, A. (2005). Analysis for Characteristics of GA-Based Learning Method of Binary Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_51
Download citation
DOI: https://doi.org/10.1007/11550822_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
Online ISBN: 978-3-540-28754-4
eBook Packages: Computer ScienceComputer Science (R0)