Abstract
This study presents an integration of Evolving Spiking Neural - Network (ESNN) with Dynamic Population Particle Swarm Optimization (DPPSO). The original ESNN framework does not automatically modulate its parameters’ optimum values. Thus, an integrated framework is proposed to optimize ESNN parameters namely, the modulation factor (mod), similarity factor (sim), and threshold factor (c). DPPSO improves the original PSO technique by implementing a dynamic particle population. Performance analysis is measured on classification accuracy in comparison with the existing methods. Five datasets retrieved from UCI machine learning are selected to simulate the classification problem. The proposed framework improves ESNN performance in regulating its parameters’ optimum values.
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References
Yegnanarayana, B.: Artificial Neural Networks. PHI Learning Pvt. Ltd., New Delhi (2009)
Huang, W., Hong, H., Song, G., Xie, K.: Deep process neural network for temporal deep learning. In: International Joint Conference on Neural Networks (IJCNN), pp. 465–472 (2014)
Dhoble, K., Nuntalid, N., Indiveri, G., Kasabov, N.: Online spatio-temporal pattern recognition with evolving spiking neural networks utilising address event representation, rank order, and temporal spike learning. In: The 2012 International Joint Conference Neural Networks (IJCNN), pp. 1–7 (2012)
Saleh, A.Y., Shamsuddin, S.M., Hamed, H.N.B.A.: Parameter tuning of evolving spiking neural network with differential evolution algorithm. In: International Conference of Recent Trends in Information and Communication Technologies, p. 13 (2014)
Hamed, H.N.A., Kasabov, N., Shamsuddin, S.M.: Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks. In: InTech (2011)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948 (1995)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: IEEE Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)
Mao, C., Lin, R., Xu, C., He, Q.: Towards a trust prediction framework for cloud services based on PSO-driven neural network. IEEE Access 5, 2187–2199 (2017)
Chen, Y.C., Jiang, J.R.: Particle swarm optimization for charger deployment in wireless rechargeable sensor networks. In: 26th International Telecommunication Networks and Applications Conference (ITNAC), pp. 231–236 (2016)
Kaur, H., Prabahakar, G.: An advanced clustering scheme for wireless sensor networks using particle swarm optimization. In: 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 387–392 (2016)
Pal, D., Verma, P., Gautam, D., Indait, P.: Improved optimization technique using hybrid ACO-PSO. In: 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 277–282 (2016)
Kasabov, N.: Evolving spiking neural networks for spatio- and spectro-temporal pattern recognition. In: 2012 6th IEEE International Conference on Intelligent Systems (IS), pp. 27–32 (2012)
Wysoski, S.G., Benuskova, L., Kasabov, N.: Adaptive learning procedure for a network of spiking neurons and visual pattern recognition. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1133–1142. Springer, Heidelberg (2006). doi:10.1007/11864349_103
Schliebs, S., Defoin-Platel, M., Kasabov, N.: Analyzing the dynamics of the simultaneous feature and parameter optimization of an evolving spiking neural network. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)
Saxena, N., Tripathi, A., Mishra, K.K., Misra, A.K.: Dynamic-PSO: an improved particle swarm optimizer. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 212–219 (2015)
Kaur, R., Arora, M.: A novel asynchronous Mc-Cdma multiuser detector with modified particle swarm optimization algorithm (MPSO). In: 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 420–425 (2016)
Soni, N., Bhatt, R., Parmar, G.: Optimal LFC system of interconnected thermal power plants using hybrid particle swarm optimization-pattern search algorithm (hPSO-PS). In: 2nd International Conference on Communication Control and Intelligent Systems (CCIS), pp. 225–229 (2016)
Song, K., Li, C., Yang, L.: Parameter estimation for multi-scale multi-lag underwater acoustic channels based on modified particle swarm optimization algorithm. In: IEEE Access (2017)
M’hamdi, B., Teguar, M., Mekhaldi, A.: Optimal design of corona ring on HV composite insulator using PSO approach with dynamic population size. IEEE Trans. Dielectr. Electr. Insul. 23, 1048–1057 (2016)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Hamed, H.N.B.A., Nuzly, H.: Novel integrated methods of evolving spiking neural network and particle swarm optimisation. Ph.D. dissertation, Auckland University of Technology (2012)
Acknowledgment
This research work was supported by Universiti Teknologi Malaysia under the Research University Grant with vot. Q.J130000.2528.11H80.
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Md. Said, N.N., Abdull Hamed, H.N., Abdullah, A. (2017). The Enhancement of Evolving Spiking Neural Network with Dynamic Population Particle Swarm Optimization. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-6502-6_8
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