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G-HABC Algorithm for Training Artificial Neural Networks

G-HABC Algorithm for Training Artificial Neural Networks

Habib Shah, Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris
Copyright: © 2012 |Volume: 3 |Issue: 3 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466610859|DOI: 10.4018/jamc.2012070101
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MLA

Shah, Habib, et al. "G-HABC Algorithm for Training Artificial Neural Networks." IJAMC vol.3, no.3 2012: pp.1-19. http://doi.org/10.4018/jamc.2012070101

APA

Shah, H., Ghazali, R., Nawi, N. M., & Deris, M. M. (2012). G-HABC Algorithm for Training Artificial Neural Networks. International Journal of Applied Metaheuristic Computing (IJAMC), 3(3), 1-19. http://doi.org/10.4018/jamc.2012070101

Chicago

Shah, Habib, et al. "G-HABC Algorithm for Training Artificial Neural Networks," International Journal of Applied Metaheuristic Computing (IJAMC) 3, no.3: 1-19. http://doi.org/10.4018/jamc.2012070101

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Abstract

Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.

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