References

Dhubkarya, D.C., D. Nagariya and R. Kapoor, 2010. Implementation of a radial basis function using VHDL. Global J. Comput. Sci. Technol., 10: 16-19.
Direct Link  |  

Eberhart, R.C. and J. Kennedy, 1995. A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, October 4-6, 1995, Nagoya, Japan, pp: 39-43.

Hamadneh, N., S. Sathasivam and O.H.Choon, 2012. Higher order logic programming in radial basis function neural network. Applied Math. Sci., 6: 115-127.
Direct Link  |  

Hamadneh, N., S. Sathasivam, U.L. Tilahun and O.H. Choon, 2012. Learning logic programming in radial basis function network via genetic algorithm. J. App. Sci., 12: 840-847.
CrossRef  |  

Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, 4: 1942-1948.
CrossRef  |  Direct Link  |  

Ling, T.L., M. Ahmad and L.Y. Heng, 2011. Quantitative determination of ammonium ion in aqueous environment using riegler's solution and artificial neural network. Sains Malaysiana, 40: 1105-1113.
Direct Link  |  

Liu, X., 2010. Radial basis function neural network based on PSO with mutation operation to solve function approximation problem. Adv. Swarm Intell., 6146: 92-99.
Direct Link  |  

Lowe, D., 1989. Adaptive radial basis function nonlinearities and the problem of generalisation. Proceedings of the 1st IEE International Conference on Artificial Neural Networks, October 16-18, 1989, London, UK., pp: 171-175.

Merz, C.J. and P.M. Murphy, 1996. UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.

Moody, J. and C.J. Darken, 1989. Fast learning in networks of locally-tuned processing units. Neural Comput., 2: 281-294.
CrossRef  |  

Parker, G.B. and Z. Lee, 2003. Evolving neural networks for hexapod leg controllers. Proceedings of the 2003 IEEE/RSJ International Conference on October 27-31, 2003, as Vegas, NV, USA, 1376-1381.

Rojas, R., 1996. Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin, Germany, ISBN-13: 9783540605058, Pages: 502.

Sathasivam, S., N. Hamadneh and O.H. Choon, 2011. Comparing neural networks: Hopfield network and RBF network. Applied Math. Sci., 5: 3439-3452.
Direct Link  |  

Schwenker, F., H.A. Kestler and G. Palm, 2001. Three learning phases for radial-basis-function networks. Neural Networks, 14: 439-458.
CrossRef  |  

Taghi, M., V. Baghmisheh and N. Pavesic, 2004. Training RBF networks with selective backpropagation. Neurocomputing, 62: 39-64.
CrossRef  |  Direct Link  |  

Tilahun, S.L., 2013. Prey-predator algorithmppa): A new metaheuristic optimization algorithm. PhD, Thesis, Universiti Sains Malaysia, Penang, Malaysia

Ustun, S.V., 2007. GA-based optimization of PI speed controller coefficients for ANN-modelled vector controlled induction motor. J. Applied Sci., 7: 4001-4006.
CrossRef  |  Direct Link  |  

Venkatesan, P. and S. Anitha, 2006. Application of a radial basis function neural network for diagnosis of diabetes mellitus. Curr. Sci., 91: 1195-1199.
Direct Link  |  

Xiaobin, L., 2009. RBF neural network optimized by particle swarm optimization for forecasting urban traffic flow. Proceedings of the 3rd International Symposium on Intelligent Information Technology Application, November 21-22, 2009, University of Technology, Nanchang, China, pp: 124-127.

Zhang, G., B.E. Patuwo and M.Y. Hu, 1998. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast., 14: 35-62.
CrossRef  |  Direct Link  |  

Zhou, Z.H. and X.Y. Liu, 2006. Training cost-sensitive neural networks with methods addressing the class imbalance problem. Knowl.Data Engin. IEEE Trans., 18: 63-77.
Direct Link  |