The Enhancement of Evolving Spiking Neural Network with Firefly Algorithm

Authors

  • Farezdzuan Roslan Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
  • Haza Nuzly Abdull Hamed Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
  • Mohd Adham Isa Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.

Keywords:

Evolving Spiking Neural Network, Firefly Algorithm, Nature Inspired Algorithms, Parameter Optimization,

Abstract

This study presents the integration between Evolving Spiking Neural Network (ESNN) and Firefly Algorithm (FA) for parameter optimization of ESNN model. Since ESNN lacks the ability to automatically select the optimum parameters, Firefly Algorithm (FA), as one of nature inspired metaheuristic algorithms is used as a new parameter optimizer for ESNN. The proposed method, ESNN-FA is used to determine the optimum value of ESNN parameter which is modulation factor (Mod), similarity factor (Sim) and threshold factor (C). Five standard datasets from UCI machine learning are used to measure the effectiveness of the proposed work. The classification results obtained shown an increase in accuracy than standard ESNN for all dataset except for iris dataset. Classification accuracy for iris dataset is 84% which less than standard ESNN with 89.33%. The rest of datasets achieved higher classification accuracy than standard ESNN which for breast cancer with 92.12% than 66.18%, diabetes with 68.25% than 38.46%, heart with 78.15% than 66.3% and wine with 78.66% than 44.45%.

References

A. Saleh, H. Hameed, M. Najib, and M. Salleh, “A novel hybrid algorithm of differential evolution with evolving spiking neural network for pre-synaptic neurons optimization,” Int. J. Adv. Soft Computing and Its Applications, vol 6, no. 1, pp. 1-16, Mar. 2014.

W. Maass, “Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons,” Inst. Theor. Comput. Sci. Tech. Univ. Graz. Graz, Austria, Tech. Rep. TR-1999-037, 1999. [Online]. Available at http//www. igi. tugraz. at/psfiles/90. pdf.

B. Schrauwen and J. Van Campenhout, “Backpropagation for population-temporal coded spiking neural networks,” in Neural Networks, 2006. IJCNN’06. International Joint Conference on, 2006, pp. 1797–1804.

W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge university press, 2002.

A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol., vol. 117, no. 4, pp. 500-544, Aug. 1952.

E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Trans. Neural Networks, vol. 14, no. 6, pp. 1569–1572, 2003.

Wysoski, Benuskova, and Kasabov, “Adaptive learning procedure for a network of spiking neurons and visual pattern recognition,” in Advanced Concepts for Intelligent Vision Systems, vol. 4179, J. BlancTalon, W. Philips, D. Popescu, and P. Scheunders, Eds. Springer Berlin Heidelberg, 2006, pp. 1133–1142.

N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer Science & Business Media, 2007.

S. G. Wysoski, L. Benuskova, and N. Kasabov, “Adaptive Spiking Neural Networks for Audiovisual Pattern Recognition,” in Neural Information Processing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 406–415.

N. F. Johari, A. M. Zain, N. H. Mustaffa, and A. Udin, “Firefly algorithm for optimization problem,” Appl. Mech. Mater., vol. 421, pp. 512–517, Sep. 2013.

L. Zhang, L. Liu, X.-S. Yang, Y. Dai, V. Zumer, and H. Zhang, “A novel hybrid firefly algorithm for global optimization,” PLoS One, vol. 11, no. 9, p. e0163230, Sep. 2016.

H. Su, Y. Cai, and Q. Du, “Firefly-algorithm-inspired framework with band selection and extreme learning machine for hyperspectral image classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, no. 1, pp. 309–320, Jan. 2017.

S. G. Wysoski, L. Benuskova, and N. Kasabov, “On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition,” in Artificial Neural Networks – ICANN 2006: 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part I, S. D. Kollias, A. Stafylopatis, W. Duch, and E. Oja, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 61–70.

S. M. Bohte, H. La Poutré, J. N. Kok, and H. La Poutre, “Errorbackpropagation in temporally encoded networks of spiking neurons,” Neurocomputing, vol. 48, no. 1, pp. 17–37, 2002.

S. THORPE and J. Gautrais, “How can the visual system process a natural scene in under 150 ms? On the role of asynchronous spike propagation,” in European symposium on artificial neural networks, 1997, pp. 79–84.

H. N. A. Hamed, N. Kasabov, and S. M. Shamsuddin, “Integrated feature selection and parameter optimization for evolving spiking neural networks using quantum inspired particle swarm optimization,” in Soft Computing and Pattern Recognition, 2009. SOCPAR’09. International Conference of, 2009, pp. 695–698.

X.-S. Yang, “Firefly algorithm,” Nature-Inspired Metaheuristic Algorithms, vol. 20, pp. 79–90, 2008.

X.-S. Yang and X. He, “Firefly algorithm: recent advances and applications,” Int. J. Swarm Intell., vol. 1, no. 1, pp. 36–50, 2013.

T. Hassanzadeh and M. R. Meybodi, “A new hybrid approach for data clustering using firefly algorithm and K-means,” in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 7–11.

G. H. John, R. Kohavi, and K. Pfleger, “Irrelevant features and the subset selection problem,” in Machine learning: Proceedings of the Eleventh International Conference, 1994, pp. 121–129.

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell., vol. 97, no. 1–2, pp. 273–324, Dec. 1997.

M. Valko, N. C. Marques, and M. Castellani, “Evolutionary feature selection for spiking neural network pattern classifiers,” in 2005 Purtuguese Conf. Artif. Intell., 2005, pp. 181–187.

K. Bache and M. Lichman, UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2013. [Online]. Available at http//archive. ics. uci. edu/ML.

H. N. A. Hamed, Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation, Auckland University of Technology, 2012.

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Published

2017-10-20

How to Cite

Roslan, F., Abdull Hamed, H. N., & Isa, M. A. (2017). The Enhancement of Evolving Spiking Neural Network with Firefly Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-3), 63–66. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2873

Issue

Vol. 9 No. 3-3: Special Issue on Software Engineering I

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Articles

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