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Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) pp 315–322Cite as

HAPSOENN: Hybrid Accelerated Particle Swarm Optimized Elman Neural Network

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 520))

Abstract

Back propagation (BP) algorithm is a very popular optimization procedure of ANN’s training process. However, traditional BP has some drawbacks such as getting stuck in local minima, and network stagnancy. Recently, some researches proposed the use of Elman Neural Network (ENN) trained with back propagation algorithm to yield faster and more accurate results during learning. Yet, the performance of ENN is still considerably dependent on initial weights in the network. In this paper, a new method known as HAPSOENN which adapts the network weights using Accelerated Particle Swarm Optimization (APSO) was proposed as a mechanism to improve the performance of ENN. The performance of the proposed algorithm is compared with Back-Propagation Neural Network (BPNN) and other similar hybrid variants on benchmarked classification datasets. The simulation results show that the proposed technique performs better and has faster convergence than other algorithms in terms of MSE and accuracy.

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Acknowledgements

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) Ministry of Higher Education (MOHE) Malaysia for financially supporting this Research under Trans-disciplinary Research Grant Scheme (TRGS) vote no. T003. This research also supported by GATES IT Solution Sdn. Bhd under its publication scheme.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Soft Computing and Data Mining Centre, Universiti Tun Hussein Onn Malaysia (UTHM), P.O. Box 101, 86400, Parit Raja, Batupahat, Johor Darultakzim, Malaysia

    Nazri Mohd. Nawi & Nurul Syazwani Muhamadan

  2. Institute of Business and Management Science, Agriculture University Peshawar, Peshawar, Pakistan

    Abdullah Khan

  3. Department of Computer Science and Information Technology, University of Lahore, Islamabad Campus, Japan Road, Sihala, Pakistan

    M. Z. Rehman

Authors
  1. Nazri Mohd. Nawi
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  2. Abdullah Khan
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  3. Nurul Syazwani Muhamadan
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  4. M. Z. Rehman
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Corresponding author

Correspondence to Nazri Mohd. Nawi .

Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia

    Mohamed Othman

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin

  6. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

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Nawi, N.M., Khan, A., Muhamadan, N.S., Rehman, M.Z. (2019). HAPSOENN: Hybrid Accelerated Particle Swarm Optimized Elman Neural Network. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_33

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