Advertisement

A Modified Artificial Bee Colony Optimization for Functional Link Neural Network Training

  • Yana Mazwin Mohmad Hassim
  • Rozaida Ghazali
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Functional Link Neural Network (FLNN) has becoming as an important tool for solving non-linear classification problem. This is due to its modest architecture which required less tunable weights for learning as compared to the standard multilayer feed forward network. The most common learning scheme for tuning the weight in FLNN is a Backpropagation (BP-learning) algorithm. However, the learning method by BP-learning algorithm tends to easily get trapped in local minima which affect the performance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony (mABC) as a learning scheme for training the FLNN network in overcoming the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network.

Keywords

Functional link neural network Modified artificial bee colony Learning scheme Training 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The authors wish to thank the Ministry of Higher Education Malaysia and Universiti Tun Hussein Onn Malaysia for the scholarship given in conducting these research activities.

References

  1. 1.
    Zhang, G.P., Neural networks for classification: a survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2000. 30(4): p. 451-462.Google Scholar
  2. 2.
    Liao, S.-H. and Wen, C.-H., Artificial neural networks classification and clustering of methodologies and applications – literature analysis from 1995 to 2005. Expert Systems with Applications, 2007. 32(1): p. 1-11.Google Scholar
  3. 3.
    Pao, Y.H. and Takefuji, Y., Functional-link net computing: theory, system architecture, and functionalities. Computer, 1992. 25(5): p. 76-79.Google Scholar
  4. 4.
    Dehuri, S. and Cho, S.-B., A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Computing & Applications 2010. 19(2): p. 187-205.Google Scholar
  5. 5.
    Mohmad Hassim, Y.M. and Ghazali, R., Using Artificial Bee Colony to Improve Functional Link Neural Network Training. Applied Mechanics and Materials, 2013. 263: p. 2102-2108.Google Scholar
  6. 6.
    Karaboga, D., An Idea Based on Honey Bee Swarm for Numerical Optimization. 2005, Erciyes University, Engineering Faculty, Computer Science Department, Kayseri/Turkiye.Google Scholar
  7. 7.
    Karaboga, D. and Basturk, B., On the performance of artificial bee colony (ABC) algorithm. Elsevier Applied Soft Computing, 2007. 8: p. 687-697.Google Scholar
  8. 8.
    Pao, Y.-H., Adaptive pattern recognition and neural networks. 1989: Addison-Wesley Longman Publishing Co., Inc. 309.Google Scholar
  9. 9.
    Misra, B.B. and Dehuri, S., Functional Link Artificial Neural Network for Classification Task in Data Mining. Journal of Computer Science, 2007. 3(12): p. 948-955.Google Scholar
  10. 10.
    Haring, S. and Kok, J. Finding functional links for neural networks by evolutionary computation. in In: Van de Merckt Tet al (eds) BENELEARN1995, proceedings of the fifth Belgian–Dutch conference on machine learning. 1995. Brussels, Belgium: pp 71–78.Google Scholar
  11. 11.
    Haring, S., Kok, J., and Van Wesel, M., Feature selection for neural networks through functional links found by evolutionary computation. In: ILiu X et al (eds) Adavnces in intelligent data analysis (IDA-97). LNCS 1280, 1997: p. 199–210.Google Scholar
  12. 12.
    Abu-Mahfouz, I.-A., A comparative study of three artificial neural networks for the detection and classification of gear faults International Journal of General Systems 2005. 34(3): p. 261-277.Google Scholar
  13. 13.
    Sierra, A., Macias, J.A., and Corbacho, F., Evolution of functional link networks. Evolutionary Computation, IEEE Transactions on, 2001. 5(1): p. 54-65.Google Scholar
  14. 14.
    Dehuri, S., Mishra, B.B., and Cho, S.-B., Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification, in Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning. 2008, Springer-Verlag: Daejeon, South Korea. p. 156-163.Google Scholar
  15. 15.
    Ghazali, R., Hussain, A.J., and Liatsis, P., Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals. Expert Systems with Applications, 2011. 38(4): p. 3765-3776.Google Scholar
  16. 16.
    Mohmad Hassim, Y.M. and Ghazali, R., Training a Functional Link Neural Network Using an Artificial Bee Colony for Solving a Classification Problems. Journal of Computing Press, NY, USA, 2012. 4(9): p. 110-115.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn Malaysia (UTHM)Batu PahatMalaysia

Personalised recommendations