Abstract
This paper attempts to enhance the learning performance of radial basis function neural network (RBFN) through swarm intelligence methods and self-organizing map (SOM) neural network (SOMnet). Further, the particle swarm optimization (PSO) and genetic algorithm (GA)-based method (i.e., PG approach) is employed to train RBFN. The proposed SOMnet + PG approach (called: SPG) algorithm combines the automatically clustering ability of SOMnet with PG approach. The simulation results revealed that SOMnet, PSO, and GA methods can be integrated ingeniously and redeveloped into a hybrid algorithm which aims for obtaining the best accurate learning performance among other algorithms in this study. On the other hand, method evaluation results for two benchmark problems and a gold price prediction case showed that the proposed SPG algorithm outperforms other algorithms and the auto-regressive integrated moving average (ARIMA) models in accuracy.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akaike, H.: A new look at the statistical model identification. IEEE Trans. on Automat. Control AC 19, 716–723 (1974)
Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Conversion and Management 78, 711–719 (2014)
Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco (1976)
Babu, C.N., Reddy, B.E.: A moving-average-filter-based hybrid ARIMA-ANN model for forecasting time series data. Applied Soft Computing (in press, 2014)
Chang, P., Liu, C., Wang, Y.: A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry. Decision Support Systems 42, 1254–1269 (2006)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks 2(2), 302–309 (1991)
Chen, S., Wu, Y., Luk, G.L.: Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks. IEEE Transactions on Neural Networks 10(5), 1239–1243 (1999)
Chu, S.C., Tsai, P.W.: Computational intelligence based on behaviors of cats. Int. J. Innov. Comput., Inform. Control 3(1), 163–173 (2007)
Co, H.C., Boosarawongse, R.: Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering 53, 610–627 (2007)
Cui, Z., Gao, X.: Theory and applications of swarm intelligence. Neur. Comput. Appl. 21(2), 205–206 (2012)
Denker, J.S.: Neural network models of learning and adaptation. Physica D 22, 216–232 (1986)
Dickey, D.A., Fuller, W.A.: Likelihood Ration Statistics for Autoregressive Time Series with A Unit Root. Econometrica 49(4), 1057–1072 (1981)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst., Man Cybernet. – Part B: Cybernet. 26, 29–41 (1996)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
Engle, R.F., Robert, F., Yoo, B.S.: Forecasting and Testing in Cointegrated Systems. Journal of Econometrics 35, 588–589 (1987)
Feng, H.M.: Self-generation RBFNs using evolutional PSO learning. Neurocomputing 70, 241–251 (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)
Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. Johns Hopkins Univ. Press, Baltimore (1996)
Jaipuria, S., Mahapatra, S.S.: An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications 41, 2395–2408 (2014)
Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer engineering Department (2005)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, pp. 1942–1948 (1995)
Khashei, M., Bijari, M.: A new class of hybrid models for time series forecasting. Expert Systems with Applications 39, 4344–4357 (2012)
Kmenta, J.: Elements of Econometrics, 2nd edn., p. 332. Macmillan Publishing Co., New York (1986)
Kohonen, T.: The Self-Organizing Map. Proc. IEEE 78(9), 1464–1480 (1990)
Kuo, R.J., Han, Y.S.: A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem-a case study on supply chain mode. Applied Mathematical Modelling 35(8), 3905–3917 (2011)
Kuo, R.J., Hu, T.-L., Chen, Z.-Y.: Sales Forecasting Using an Evolutionary Algorithm Based Radial Basis Function Neural Network. In: Yang, J., Ginige, A., Mayr, H.C., Kutsche, R.-D. (eds.) Information Systems: Modeling, Development, and Integration. LNBIP, vol. 20, pp. 65–74. Springer, Heidelberg (2009)
Kuo, R.J., Syu, Y.J., Chen, Z.Y., Tien, F.C.: Integration of Particle Swarm Optimization and Genetic Algorithm for Dynamic Clustering. Information Sciences 195, 124–140 (2012)
Lee, Z.J.: A novel hybrid algorithm for function approximation. Expert Systems with Applications 34, 384–390 (2008)
Li, X.M., Xiao, R.B., Yuan, S.H., Chen, J.A., Zhou, J.X.: Urban total ecological footprint forecasting by using radial basis function neural network: A case study of Wuhan city, China. Ecological Indicators 10, 241–248 (2010)
Lin, C.F., Wu, C.C., Yang, P.H., Kuo, T.Y.: Application of Taguchi method in light-emitting diode backlight design for wide color gamut displays. Journal of Display Technology 5(8), 323–330 (2009)
Lin, G.F., Wu, M.C.: An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model. Journal of Hydrology 405, 439–450 (2011)
Looney, C.G.: Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowledge Data Eng. 8(2), 211–226 (1996)
Lopez, M., Valero, S., Senabre, C., Aparicio, J., Gabaldon, A.: Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study. Electric Power Systems Research 91, 18–27 (2012)
Lu, C.J., Wang, Y.W.: Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. Int. J. Production Economics 128, 603–613 (2010)
Moradkhani, H., Hsu, K.L., Gupta, H.V., Sorooshian, S.: Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology 295, 246–262 (2004)
Olabi, A.G.: Using Taguchi method to optimize welding pool of dissimilar laser-welded components. Opt. Laser Technol. 40, 379–388 (2008)
Pan, Q.K., Wang, L., Mao, K., Zhao, J.H., Zhang, M.: An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process. IEEE Trans. on Automation Science and Engineering 10(2), 307–322 (2013)
Qasem, S.N., Shamsuddin, S.M., Zain, A.M.: Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowledge-Based Systems 27, 475–497 (2012)
Qiu, X., Lau, H.Y.K.: An AIS-based hybrid algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing 25, 489–503 (2014)
Rumbell, T., Denham, S.L., Wennekers, T.: A Spiking Self-Organizing Map Combining STDP, Oscillations, and Continuous Learning. IEEE Trans. on Neural, Networks and Learning Systems 25(5), 894–907 (2014)
Sarimveis, H., Alexandridis, A., Mazarakis, S., Bafas, G.: A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms. Computers and Chemical Engineering 28, 209–217 (2004)
Shelokar, P.S., Siarry, P., Jayaraman, V.K., Kulkarni, B.D.: Particle swarm and colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation 188, 129–142 (2007)
Taguchi, G., Yokoyama, T.: Taguchi Methods: Design of Experiments. ASI Press, Dearbon (1993)
Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Applied Soft Computing 11(2), 2625–2632 (2011)
Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Information Sciences 279, 587–603 (2014)
Whitehead, B.A., Choate, T.D.: Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Trans. Neural Networks 7(4), 869–880 (1996)
Xu, R., Venayagamoorthy, G.K., Wunsch, D.C.: Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Networks 20, 917–927 (2007)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Yadav, V., Srinivasan, D.: A SOM-based hybrid linear-neural model for short-term load forecasting. Neurocomputing 74, 2874–2885 (2011)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Insp. Comput. 2(2), 78–84 (2010)
Yu, L., Wang, S., Lai, K.K., Wen, F.: A multiscale neural network learning paradigm for financial crisis forecasting. Neurocomputing 73, 716–725 (2010)
Zou, H.F., Xia, G.P., Yang, F.T., Wang, H.Y.: An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing 70, 2913–2923 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, ZY. (2014). A Hybrid Algorithm by Combining Swarm Intelligence Methods and Neural Network for Gold Price Prediction. In: Wang, L.SL., June, J.J., Lee, CH., Okuhara, K., Yang, HC. (eds) Multidisciplinary Social Networks Research. MISNC 2014. Communications in Computer and Information Science, vol 473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45071-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-662-45071-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45070-3
Online ISBN: 978-3-662-45071-0
eBook Packages: Computer ScienceComputer Science (R0)