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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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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