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Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 549))

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Abstract

This paper presents a Metahybrid algorithm that consists of the dual combination of Wolf Search (WS) and Elman Recurrent Neural Network (ERNN). ERNN is one of the most efficient feed forward neural network learning algorithm. Since ERNN uses gradient descent technique during the training process; therefore, it is not devoid of local minima and slow convergence problem. This paper used a new metaheuristic search algorithm, called wolf search (WS) based on wolf’s predatory behavior to train the weights in ERNN to achieve faster convergence and to avoid the local minima. The performance of the proposed Metahybrid Wolf Search Elman Recurrent Neural Network (WRNN) is compared with Bat with back propagation (Bat-BP) algorithm and other hybrid variants on benchmark classification datasets. The simulation results show that the proposed Metahybrid WRNN algorithm has better performance in terms of CPU time, accuracy and MSE than the other algorithms.

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Acknowledgments

The Authors would like to thank Office of Research, Innovation, Commercialization and Consultancy (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Education (MOE) Malaysia for financially supporting this Research under Fundamental Research Grant Scheme (FRGS) vote no. 1236. This research is also supported by Gates IT Solution Sdn. Bhd under its publication scheme.

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Authors and Affiliations

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

    Nazri Mohd. Nawi, M. Z. Rehman, Norhamreeza Abdul Hamid, Abdullah Khan, Rashid Naseem & Jamal Uddin

Authors
  1. Nazri Mohd. Nawi
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  2. M. Z. Rehman
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  3. Norhamreeza Abdul Hamid
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  4. Abdullah Khan
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  5. Rashid Naseem
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  6. Jamal Uddin
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Corresponding author

Correspondence to Nazri Mohd. Nawi .

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Editors and Affiliations

  1. Department of Information System, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  3. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  4. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

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Nawi, N.M., Rehman, M.Z., Hamid, N.A., Khan, A., Naseem, R., Uddin, J. (2017). Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-51281-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51279-2

  • Online ISBN: 978-3-319-51281-5

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