Implementation of Modified Cuckoo Search Algorithm on Functional Link Neural Network for Temperature and Relative Humidity Prediction

  • Siti Zulaikha Bt Abu Bakar
  • Rozaida Bt Ghazali
  • Lokman Hakim Bin Ismail
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

The impact of temperature and relative humidity changes bringing a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure environmental changes. Neural network, especially the Multilayer Perceptron (MLP) which uses Back Propagation algorithm (BP) as a supervised learning method, has been successfully applied in various problem for meteorological prediction tasks. However, this architecture still facing problem where the convergence rate is very low due to the multilayering topology of the network. Thus, this study proposes an implementation of Functional Link Neural Network (FLNN) which composes of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS). The FLNN is used to predict the daily temperatures and relative humidity of Batu Pahat region. Extensive simulation results have been compared with standard MLP trained with the BP, and FLNN with that of BP. Promising results have shown that FLNN when trained with the MCS has successfully outperformed other network models with reduced prediction error and fast convergence rate.

Keywords

Neural network Multi-layer perceptron Higher order neural networks Functional link neural network Back propagation algorithm Modified cuckoo search 

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Notes

Acknowledgments

The author would like to thank Ministry of Higher Education for the Economical support under grant number vote 0882.

References

  1. 1.
    Hayati,M., Mohebi,Z. 2007. Temperature Forecasting Based on Neural Network Approach in World Applied Sciences Journal 2 (6):613-620, 2007. IBSN:1818-4952. IDOSI Publications 2007Google Scholar
  2. 2.
    Kazemijad,M., Deghan,M., Motamadinejad,M.B., Rastegar,H., 2006. A New Short Term Load Forecasting Using Multilayer Perceptron in ICIA.Google Scholar
  3. 3.
    Husaini,N.A., Ghazali,R., Ismail,L.H. (2011). An Application of Pi-Sigma Neural Network for The Prediction of Flood Disaster. University Tun Hussein Onn Malaysia.Google Scholar
  4. 4.
    Climate change scenarios for Malaysia: 2001-2099. Malaysian Meteorological department scientific report, January 2009.Google Scholar
  5. 5.
    Paras,Mathur,S., Kumar,A. & Chandra,M., 2007. A Feature Based Neural Network Model for Weather Forecasting in World Academy of Science Engineering and Technology.Google Scholar
  6. 6.
    Giles, C. L. and Maxwell, T. (1987) Learning, invariance and generalization in high-order neural networks. In Applied Optics, vol. 26, no. 23, Optical Society of America, Washington DC, pp. 4972-4978.Google Scholar
  7. 7.
    W. A. C. Schmidt & J.P. Davis. “Pattern Recognition Properties of Various Feature Spaces for Higher Order Neural Networks.” In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 8, August 1993Google Scholar
  8. 8.
    Y.Pao. “Adaptive Patten Recognition and Neural Networks.” Addison-Wesley, USA, 1989. ISBN: 0 2010125846Google Scholar
  9. 9.
    Payne, Robert B. (2005). The Cuckoos. OxfordUniversity Press. ISBN 0-19-850213-3. Retrieved 2007-12-19.Google Scholar
  10. 10.
    Yang X-S, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 2010; 1:330–43.Google Scholar
  11. 11.
    S. Walton, O. Hassan, K. Morgan, M.R. Brown (2011). Modified cuckoo search: A new gradient free optimisation algorithm. Chaos, Solitons & Fractals 44, p. 710–71Google Scholar
  12. 12.
    X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, p. 210–214, 2009.Google Scholar
  13. 13.
    Z. Xiang, G. Bi, and T. Le-Ngoc, “Polynomial perceptrons and their applications to fading channel equalization and cochannel interference suppression,” IEEE Transactions on Signal Processing, vol. 42, no. 9, pp. 2470–2479, 1994.Google Scholar
  14. 14.
    J. C. Patra and C. Bornand, “Nonlinear dynamic system identification using Legendre neural network,” in Neural Networks (IJCNN), The 2010 International Joint Conference on, 2010, pp. 1-7.Google Scholar
  15. 15.
    Ghazali, R., Hussain, A., and El-Dereby, W. (2006). “Application of Ridge Polynomial Neural Networks to Financial Time Series Prediction.” International Joint Conference on Neural Networks (IJCNN ‘06). 913-920.Google Scholar
  16. 16.
    P. P. Raghu, et al., “A combined neural network approach for texture classification,” Neural Networks, vol. 8, pp. 975-987, 1995.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Siti Zulaikha Bt Abu Bakar
    • 1
  • Rozaida Bt Ghazali
    • 1
  • Lokman Hakim Bin Ismail
    • 1
  1. 1.Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

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