Using Artificial Bee Colony to Improve Functional Link Neural Network Training

Abstract:

Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 2102-2108
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.2102
• Online since: December 2012
• Authors: Yana Mazwin Mohmad Hassim*, Rozaida Ghazali
• Keywords: Artificial Bee Colony, Functional Link Neural Network, Learning, Neural Network (NN)

References

[1] G. P. Zhang, "Neural networks for classification: a survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 30, pp.451-462, 2000.

DOI: 10.1109/5326.897072

[2] S.-H. Liao and C.-H. Wen, "Artificial neural networks classification and clustering of methodologies and applications – literature analysis from 1995 to 2005," Expert Systems with Applications, vol. 32, pp.1-11, 2007.

DOI: 10.1016/j.eswa.2005.11.014

[3] J. C. Patra and R. N. Pal, "A functional link artificial neural network for adaptive channel equalization," Signal Processing, vol. 43, pp.181-195, 1995.

DOI: 10.1016/0165-1684(94)00152-p

[4] S. Dehuri and S.-B. Cho, "A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN," Neural Computing & Applications vol. 19, pp.187-205, 2010.

DOI: 10.1007/s00521-009-0288-5

[5] Y. H. Pao and Y. Takefuji, "Functional-link net computing: theory, system architecture, and functionalities," Computer, vol. 25, pp.76-79, 1992.

DOI: 10.1109/2.144401

[6] S. Haykin, "Neural Networks: A Comprehensive Foundation. ," The Knowledge Engineering Review vol. 13, pp.409-412, 1999.

[7] E. Sahin, "A New Higher-order Binary-input Neural Unit: Learning and Generalizing Effectively via Using Minimal Number of Monomials " Master, Middle East Technical University of Ankara, 1994.

[8] B. B. Misra and S. Dehuri, "Functional Link Artificial Neural Network for Classification Task in Data Mining," Journal of Computer Science, vol. 3, pp.948-955, 2007.

DOI: 10.3844/jcssp.2007.948.955

[9] Y. H. Pao, "Adaptive pattern recognition and neural networks," 1989.

[10] 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.

DOI: 10.1109/ijcnn.2010.5596904

[11] J. C. Patra and A. C. Kot, "Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 32, pp.505-511, 2002.

DOI: 10.1109/tsmcb.2002.1018769

[12] H. M. Abbas, "System Identification Using Optimally Designed Functional Link Networks via a Fast Orthogonal Search Technique," JOURNAL OF COMPUTERS, vol. 4, FEBRUARY 2009 2009.

DOI: 10.4304/jcp.4.2.147-153

[13] S. Emrani, et al., "Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems," in Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, 2010, pp.35-40.

DOI: 10.1109/iciea.2010.5514748

[14] S. J. Nanda, et al., "Improved Identification of Nonlinear MIMO Plants using New Hybrid FLANN-AIS Model," in Advance Computing Conference, 2009. IACC 2009. IEEE International, 2009, pp.141-146.

DOI: 10.1109/iadcc.2009.4808996

[15] J. Teeter and C. Mo-Yuen, "Application of functional link neural network to HVAC thermal dynamic system identification," Industrial Electronics, IEEE Transactions on, vol. 45, pp.170-176, 1998.

DOI: 10.1109/41.661318

[16] P. P. Raghu, et al., "A combined neural network approach for texture classification," Neural Networks, vol. 8, pp.975-987, 1995.

DOI: 10.1016/0893-6080(95)00013-p

[17] I.-A. Abu-Mahfouz, "A comparative study of three artificial neural networks for the detection and classification of gear faults " International Journal of General Systems vol. 34, pp.261-277, 2005.

DOI: 10.1080/03081070500065726

[18] L. M. Liu, et al., "Image classification in remote sensing using functional link neural networks," in Image Analysis and Interpretation, 1994., Proceedings of the IEEE Southwest Symposium on, 1994, pp.54-58.

DOI: 10.1109/iai.1994.336685

[19] S. Dehuri and S.-B. Cho, "Evolutionarily optimized features in functional link neural network for classification," Expert Systems with Applications, vol. 37, pp.4379-4391, 2010.

DOI: 10.1016/j.eswa.2009.11.090

[20] M. Klaseen and Y. H. Pao, "The functional link net in structural pattern recognition," in TENCON 90. 1990 IEEE Region 10 Conference on Computer and Communication Systems, 1990, pp.567-571 vol.2.

DOI: 10.1109/tencon.1990.152674

[21] G. H. Park and Y. H. Pao, "Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net," Neurocomputing, vol. 31, pp.45-65, 2000.

DOI: 10.1016/s0925-2312(99)00149-6

[22] R. Majhi, et al., "Development and performance evaluation of FLANN based model for forecasting of stock markets," Expert Systems with Applications, vol. 36, pp.6800-6808, 2009.

DOI: 10.1016/j.eswa.2008.08.008

[23] R. Ghazali, et al., "Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals," Expert Systems with Applications, vol. 38, pp.3765-3776, 2011.

DOI: 10.1016/j.eswa.2010.09.037

[24] A. Namatame and N. Veda, "Pattern classification with Chebyshev neural network," International Jounal of Neural Network, vol. 3, p.23–31, 1992.

[25] S. Haring and J. Kok, "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, Brussels, Belgium, 1995.

[26] S. Haring, et al., "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, p.199–210, 1997.

DOI: 10.1007/bfb0052841

[27] A. Sierra, et al., "Evolution of functional link networks," Evolutionary Computation, IEEE Transactions on, vol. 5, pp.54-65, 2001.

[28] S. Dehuri, et al., "Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification," presented at the Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning, Daejeon, South Korea, 2008.

DOI: 10.1007/978-3-540-88906-9_20

[29] D. Pham, et al., "The Bees Algorithm," Manufacturing Engineering Centre, Cardiff University, UK,2005.

[30] D. Karaboga, "An Idea Based on Honey Bee Swarm for Numerical Optimization," Erciyes University, Engineering Faculty, Computer Science Department, Kayseri/Turkiye2005.

[31] D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Elsevier Applied Soft Computing, vol. 8, pp.687-697, 2007.

DOI: 10.1016/j.asoc.2007.05.007

[32] B. Akay and D. Karaboga, "A modified Artificial Bee Colony algorithm for real-parameter optimization," Information Sciences, vol. In Press, Corrected Proof, 2010.

DOI: 10.1016/j.ins.2010.07.015

[33] D. Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied Soft Computing, vol. 11, pp.652-657, 2011.

DOI: 10.1016/j.asoc.2009.12.025