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Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
Pure and Applied Mathematics Journal
Volume 6, Issue 6, December 2017, Pages: 154-159
Received: Sep. 18, 2017; Accepted: Oct. 11, 2017; Published: Nov. 14, 2017
Authors
Mengshan Li, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Genqin Sun, Library of Gannan Normal University, Gannan Normal University, Ganzhou, China
Huaijin Zhang, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Keming Su, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Bingsheng Chen, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
Yan Wu, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
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Abstract
Petroleum price are affected by some uncertainties and nonlinear factors, how to predict the price effectively is the focus of the present study. In this paper, a 3 layers back propagation artificial neural network model based on particle swarm optimization algorithm combined with chaos theory and self-adaptive weight strategy is developed, the model structure is 7-13-1, and used to predict the petroleum price. By comparing with the other models, it shows that the model proposed in this paper has good prediction performance, the prediction accuracy and correlations are better.
Keywords
Petroleum Price, Prediction Model, Particle Swarm Optimization, Neural Network
Mengshan Li, Genqin Sun, Huaijin Zhang, Keming Su, Bingsheng Chen, Yan Wu, Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm, Pure and Applied Mathematics Journal. Vol. 6, No. 6, 2017, pp. 154-159. doi: 10.11648/j.pamj.20170606.11
References
[1]
M. Kendix, Walls, W. D. Estimating the impact of refinery outages on petroleum product prices. Energy Economics, 2010, 32(6): 1291-1298.
[2]
J. Y. Seo Diversification of Crude Oil Import Sources as Determinant Factors in the Pricing of Petroleum Products. Energy Sources Part B-Economics Planning and Policy, 2013, 8(4): 320-327.
[3]
D. Hosken, Silvia, L., Taylor, C. Does Concentration Matter? Measurement of Petroleum Merger Price Effects. American Economic Review, 2011, 101(3): 45-50.
[4]
S. Wlazlowski, Giulietti, M., Binner, J., Milas, C. Price dynamics in European petroleum markets. Energy Economics, 2009, 31(1): 99-108.
[5]
C. Hendrickson Petroleum prices and transportation engineering. Journal Of Transportation Engineering-asce, 2008, 134(9): 359-360.
[6]
S. H. Kang, Yoon, S. M. Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 2013, 36: 354-362.
[7]
H. D. Kurz, Salvadori, H. Classical economics and the problem of exhaustible resources Metro economica, 2001, 52(3): 282-296.
[8]
P. Robert The long-run evolution of energy prices. The energy journal, 1999, 20(2): 1-27.
[9]
C. W. Yang, Wang, M. J., Huang, B. N. An analysis of factors affecting price volatility of the US oil market. Energy Economics, 2002, (24): 107-119.
[10]
A. Salah, Hamid, B. On the predictive accuracy of crude oil future prices. Energy Policy, 2004, (32): 1389-1394.
[11]
A. M. Ulph, Folie, G. M. Exhaustible resource and cartels: an intertemporal nash-cournot model. The Canadian journal of economics,, 1980, 13(4): 645-658.
[12]
N. I. Al-Bulushi, King, P. R., Blunt, M. J., Kraaijveld, M. Artificial neural networks workflow and its application in the petroleum industry. Neural Computing & Applications, 2012, 21(3): 409-421.
[13]
S. Mohaghegh, Arefi, R., Ameri, S., Aminiand, K., Nutter, R. Petroleum reservoir characterization with the aid of artificial neural networks. Journal Of Petroleum Science And Engineering, 1996, 16(4): 263-274.
[14]
U. R. Chaudhuri, Ghosh, D. Modeling & Simulation of a Crude Petroleum Desalter using Artificial Neural Network. Petroleum Science And Technology, 2009, 27(11): 1233-1250.
[15]
S. Arefi-Oskoui, Khataee, A., Vatanpour, V. Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid. ACS Combinatorial Science, 2017, 19(7): 464-477.
[16]
A. S. Ghareb, Abu Bakar, A., Hamdan, A. R. Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Systems With Applications, 2016, 49: 31-47.
[17]
M. A. Mohiuddin, Khan, S. A., Engelbrecht, A. P. Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem. Applied Intelligence, 2016, 45(3): 598-621.
[18]
M. Saidi-Mehrabad, Dehnavi-Arani, S., Evazabadian, F., Mahmoodian, V. An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 2015, 86: 2-13.
[19]
N. Netjinda, Achalakul, T., Sirinaovakul, B. Particle Swarm Optimization inspired by starling flock behavior. Applied Soft Computing, 2015, 35: 411-422.
[20]
K. R. Harrison, Engelbrecht, A. P., Ombuki-Berman, B. M. Inertia weight control strategies for particle swarm optimization. Swarm Intelligence, 2016, 10(4): 267-305.
[21]
H. R. Ahmed, Glasgow, J. I. The Agile particle swarm optimizer applied to proteomic pattern matching and discovery. Soft Computing, 2016, 20(12): 4791-4811.
[22]
Y. J. Zheng, Chen, S. Y. Cooperative particle swarm optimization for multiobjective transportation planning. Applied Intelligence, 2013, 39(1): 202-216.
[23]
M. W. Li, Kang, H. G., Zhou, P. F., Hong, W. C. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm. Journal of Systems Engineering and Electronics, 2013, 24(2): 324-334.
[24]
M. Daneshyari Chaotic neural network controlled by particle swarm with decaying chaotic inertia weight for pattern recognition. Neural Computing & Applications, 2010, 19(4): 637-645.
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