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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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References

  1. Nawi, N.M., Khan, A., Rehman, M.Z.: A new optimized Cuckoo Search Recurrent Neural Network (CSRNN) algorithm. In: The 8th International Conference on Robotic, Vision, Signal Processing and Power Applications, pp. 335–341. Springer, Singapore (2013)

    Google Scholar 

  2. Radhika, Y., Shashi, M.: Atmospheric temperature prediction using support vector machines. Int. J. Comput. Theory Eng. 1, 55–58 (2009)

    Article  Google Scholar 

  3. Akcayol, M.A., Cinar, C.: Artificial neural network based modeling of heated catalytic converter performance. Appl. Therm. Eng. 25, 2341–2350 (2005)

    Article  Google Scholar 

  4. Rehman, M.Z., Nawi, N.M.: Improving the accuracy of Gradient Descent Back Propagation algorithm (GDAM) on classification problems. Int. J. New Comput. Archit. Appl. 1, 861–870 (2011)

    Google Scholar 

  5. Kosko, B.: Neural Network and Fuzzy System. Prentice Hall, Upper Saddle River (1994)

    MATH  Google Scholar 

  6. Krasnopolsky, V.M., Chevallier, F.: Some neural network applications in environmental sciences. part II: advancing computational efficiency of environmental numerical models. Neural Netw. 16, 335–348 (2003)

    Article  Google Scholar 

  7. Coppin, B.: Artificial Intelligence Illuminated. Jones and Bartlett Publishers Inc., Sudbury (2004)

    Google Scholar 

  8. Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3–31 (2000)

    Article  Google Scholar 

  9. He, Z., Wu, M., Gong, B.: Neural network and its application on machinery fault diagnosis. In: IEEE International Conference on Systems Engineering. pp. 576–579 (1992)

    Google Scholar 

  10. Li, B., Chow, M.Y., Tipsuwan, Y., Hung, J.C.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47, 1060–1069 (2000)

    Article  Google Scholar 

  11. Nawi, N.M., Khan, A., Rehman, M.Z.: CSBPRNN: a new hybridization technique using cuckoo search to train back propagation recurrent neural network. In: Herawan, T., Deris, M.M., Abawajy, J. (eds.) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). LNEE, vol. 285, pp. 111–118. Springer, Singapore (2014). doi:10.1007/978-981-4585-18-7_13

    Chapter  Google Scholar 

  12. Zhang, J., Lok, T., Lyu, M.R.: A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185, 1026–1037 (2007)

    MATH  Google Scholar 

  13. Ab Aziz, M.F., Hj Shamsuddin, S.M., Alwee, R.: Enhancement of particle swarm optimization in elman recurrent network with bounded Vmax function. In: Proceedings 2009 3rd Asia International Conference on Modelling and Simulation, AMS 2009, pp. 125–130 (2009)

    Google Scholar 

  14. Sutskever, I., Hinton, G., Taylor, G.: The Recurrent temporal restricted Boltzmann machine. Neural Inf. Process. Syst. 21, 1601–1608 (2008)

    Google Scholar 

  15. Gupta, L., McAvoy, M., Phegley, J.: Classification of temporal sequences via prediction using the simple recurrent neural network. Pattern Recognit. 33, 1759–1770 (2000)

    Article  Google Scholar 

  16. Saad, E.W., Prokhorov II, D., Donald, C.W.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. Neural Netw. 9, 1456–1470 (1998)

    Article  Google Scholar 

  17. Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193, 156–163 (2010)

    Article  Google Scholar 

  18. Güler, N.F., Übeyli, E.D., Güler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29, 506–514 (2005)

    Article  Google Scholar 

  19. Übeyli, E.D.: Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals. Expert Syst. Appl. 34, 2538–2544 (2008)

    Article  Google Scholar 

  20. Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) optimization algorithm for training feed-forward neural networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73729-2

    Chapter  Google Scholar 

  21. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)

    MathSciNet  MATH  Google Scholar 

  22. Nawi, N.M., Rehman, M.Z., Khan, A.: A new Bat Based Back-Propagation (BAT-BP) algorithm. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J.M. (eds.) Advances in Systems Science, pp. 395–404. Springer, Cham (2014)

    Chapter  Google Scholar 

  23. Tang, R., Fong, S., Yang, X.-S., Deb, S.: Wolf search algorithm with ephemeral memory. In: Seventh International Conference on Digital Information Management (ICDIM 2012), pp. 165–172 (2012)

    Google Scholar 

  24. Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. U.S.A. 87, 9193–9196 (1990)

    Article  MATH  Google Scholar 

  25. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Article  Google Scholar 

  26. Fisher, R.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)

    Article  Google Scholar 

  27. Quinlan, J.R.: Simplifying decision trees. Int. J. Man-Mach. Stud. Spec. Issue: Knowl. Acquisition Knowl.-Based Syst. Part 5 27(3), 221–234 (1987)

    Article  Google Scholar 

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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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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

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