Abstract
A new metaheuristic based back-propagation algorithm known as Bat-BP is presented in this paper. The proposed Bat-BP algorithm successfully solves the problems like slow convergence to global minima and network stagnancy in back-propagation neural network (BPNN) algorithm. In this paper, the bat population is increased from 10 to 500 bats to detect the performance decline or incline in the Bat-BP algorithm by performing simulations on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in Bat-BP is directly proportional with an increase in bats on 2-bit XOR dataset. In case of 3-bit XOR and 4-bit OR datasets, the results deteriorated with an increase in the bat population.
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Acknowledgments
The Authors would like to thank Office of Research, Innovation, Commercialization and Consultancy Office (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia for financially supporting this Research under Fundamental Research Grant Scheme (FRGS) vote no. 1236.
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© 2014 Springer Science+Business Media Singapore
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Nawi, N.M., Rehman, .Z., Khan, A. (2014). The Effect of Bat Population in Bat-BP Algorithm. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_34
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DOI: https://doi.org/10.1007/978-981-4585-42-2_34
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