American Institute of Mathematical Sciences

Advanced
December  2015, 8(6): 1213-1221. doi: 10.3934/dcdss.2015.8.1213

Prediction method based on optimization theory and its application

Chen Li 1, , Fajie Wei 1, and  Shenghan Zhou 2,

1. 

School of Economics and Management, Beihang University, Beijing 100191, China, China
2. 

School of Reliability and System Engineering, Beihang University, Beijing 100191, China

Received  July 2015 Revised  September 2015 Published  December 2015

BP neural network is a kind of prediction method, but it has some disadvantages. So an improved particle swarm optimization scheme is used to optimize neural network parameters and structure. Both training convergence speed and classification accuracy is improved. Interest rate is always the focus in the economic research. The proposed scheme is used for seven days interbank offered rate prediction in China and the result shows that the BP neural network based on improved PSO has higher accuracy than the BP algorithm, which can provide important reference for central bank to work out monetary policy.
Keywords: BP neural network, prediction accuracy., particle swarm optimization.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Chen Li, Fajie Wei, Shenghan Zhou. Prediction method based on optimization theory and its application. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1213-1221. doi: 10.3934/dcdss.2015.8.1213
References:
[1]

M. L. Bech and E. Atalay, The topology of the federal funds market, Physica A: Statistical Mechanics and its Applications, 389 (2010), 5223-5246. Google Scholar

[2]

C. A. C. Coello, et al., Use of particle swarm optimization to design combinational logic circuits, Lecture Notes in Computer Science (S0302-9743), 2606 (2003), 398-409. doi: 10.1007/3-540-36553-2_36.  Google Scholar

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K. W. Chau, Application of a PSO-based neural network in analysis of outcomes of construction claims, Automation in Construction (S0926-5805), 16 (2007), 642-646. doi: 10.1016/j.autcon.2006.11.008.  Google Scholar

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F. van den Bergh, Particle Swarm Weight Initialization in Multi-layer Neural Networks, Development and Pracrice of Artificial Intelligence Techniques, Durban, 1999. Google Scholar

[5]

F. Fvanden Bergh and A. P. Engelbrecht, Cooperative learning in neural networks using particle swarm optimizers, South Africa Computer J., 26 (2000), 84-90. Google Scholar

[6]

D. K. Grafiotis and W. Cedeno, Feature selection for structure-activity correlation using binary particle swarm, Med. Chem., 45 (2002), 1098-1107. doi: 10.1021/jm0104668.  Google Scholar

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D. Gies and R. Yahya, Particle swarm optimization for reconfigurable phase differentiated array design, Microwave and Optical Technology Letters (S0895-2477), 38 (2003), 168-175. doi: 10.1002/mop.11005.  Google Scholar

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T. Gravelle and J. C. Morley, A Kalman filter approach to characterizing the Canadian term structure of interest rates, Applied Financial Economics, 15 (2005), 691-705. doi: 10.1080/09603100500107917.  Google Scholar

[9]

Z. He, C. Wei and L. Yang, et al., Extracting rules from fuzzy neural network by particle swarm optimization, in Proceedings of IEEE Congress on Evolutionary Computation, Anchorage, Alaska, USA, (1998), 74-77. doi: 10.1109/ICEC.1998.699325.  Google Scholar

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R. Han and Q. Ling, A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information, Applied Mathematics and Computation, 205 (2008), 792-798. doi: 10.1016/j.amc.2008.05.025.  Google Scholar

[11]

Y. Lin, W. Chang and J. Hsieh, A particle swarm optimization approach to nonlinear rational filter modeling, Expert Systems with Applications (S0957-4174), 34 (2008), 1194-1199. doi: 10.1016/j.eswa.2006.12.004.  Google Scholar

[12]

K. E. Parsopoulos, V. P. Plagianakos and G. D. Magoulas, et al., Improving the particle swarm optimizer by function "stretching", in Advances in Convex Analysis and Global Optimization, Springer-Verlag, 2001, 445-457. doi: 10.1007/978-1-4613-0279-7_28.  Google Scholar

[13]

R. J. Shiller, J. Y. Campbell, K. L. Schoenholtz and L. Weiss, Forward rates and future policy, interpreting the term structure of interest rates, Brookings Papers on Economic Activity, (1983), 173-223. Google Scholar

[14]

S. Wang, N. Feng and A. Li, The BP network learning algorithm based on particle swarm optimization, Journal of Computer Applications and Software, 20 (2003), 74-76. Google Scholar

[15]

V. Tandon, Closing the Gap between CAD/ CAM and Optimized CNC End Milling Indianapolis, USA: Purdue School of Engineering and Technology, Indiana University Purdue University, 2001. Google Scholar

[16]

D. Tsou and C. MacNish, Adaptive particle swarm optimization for high-dimensional highly convex search spaces, Proc. IEEE Int Conf on Intelligence Symposium. Canberra, 2 (2003), 783-798. doi: 10.1109/CEC.2003.1299747.  Google Scholar

[17]

Z. W. Wang, G. L. Durst and R. C. Eberhart, Particle Swarm Optimization and Neural Network Application for QSAR, Proc. IEEE Int. Conf. on Parallel and Distributed Processing Symposium, New Mexico, 2004. doi: 10.1109/IPDPS.2004.1303214.  Google Scholar

[18]

J. Wang, Q. Shen and H. Shen, et al., RBF neural network design based on the multiple population co-evolution particle swarm algorithm, Control Theory and Applications, 32 (2006), 251-255. Google Scholar

show all references

References:
[1]

M. L. Bech and E. Atalay, The topology of the federal funds market, Physica A: Statistical Mechanics and its Applications, 389 (2010), 5223-5246. Google Scholar

[2]

C. A. C. Coello, et al., Use of particle swarm optimization to design combinational logic circuits, Lecture Notes in Computer Science (S0302-9743), 2606 (2003), 398-409. doi: 10.1007/3-540-36553-2_36.  Google Scholar

[3]

K. W. Chau, Application of a PSO-based neural network in analysis of outcomes of construction claims, Automation in Construction (S0926-5805), 16 (2007), 642-646. doi: 10.1016/j.autcon.2006.11.008.  Google Scholar

[4]

F. van den Bergh, Particle Swarm Weight Initialization in Multi-layer Neural Networks, Development and Pracrice of Artificial Intelligence Techniques, Durban, 1999. Google Scholar

[5]

F. Fvanden Bergh and A. P. Engelbrecht, Cooperative learning in neural networks using particle swarm optimizers, South Africa Computer J., 26 (2000), 84-90. Google Scholar

[6]

D. K. Grafiotis and W. Cedeno, Feature selection for structure-activity correlation using binary particle swarm, Med. Chem., 45 (2002), 1098-1107. doi: 10.1021/jm0104668.  Google Scholar

[7]

D. Gies and R. Yahya, Particle swarm optimization for reconfigurable phase differentiated array design, Microwave and Optical Technology Letters (S0895-2477), 38 (2003), 168-175. doi: 10.1002/mop.11005.  Google Scholar

[8]

T. Gravelle and J. C. Morley, A Kalman filter approach to characterizing the Canadian term structure of interest rates, Applied Financial Economics, 15 (2005), 691-705. doi: 10.1080/09603100500107917.  Google Scholar

[9]

Z. He, C. Wei and L. Yang, et al., Extracting rules from fuzzy neural network by particle swarm optimization, in Proceedings of IEEE Congress on Evolutionary Computation, Anchorage, Alaska, USA, (1998), 74-77. doi: 10.1109/ICEC.1998.699325.  Google Scholar

[10]

R. Han and Q. Ling, A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information, Applied Mathematics and Computation, 205 (2008), 792-798. doi: 10.1016/j.amc.2008.05.025.  Google Scholar

[11]

Y. Lin, W. Chang and J. Hsieh, A particle swarm optimization approach to nonlinear rational filter modeling, Expert Systems with Applications (S0957-4174), 34 (2008), 1194-1199. doi: 10.1016/j.eswa.2006.12.004.  Google Scholar

[12]

K. E. Parsopoulos, V. P. Plagianakos and G. D. Magoulas, et al., Improving the particle swarm optimizer by function "stretching", in Advances in Convex Analysis and Global Optimization, Springer-Verlag, 2001, 445-457. doi: 10.1007/978-1-4613-0279-7_28.  Google Scholar

[13]

R. J. Shiller, J. Y. Campbell, K. L. Schoenholtz and L. Weiss, Forward rates and future policy, interpreting the term structure of interest rates, Brookings Papers on Economic Activity, (1983), 173-223. Google Scholar

[14]

S. Wang, N. Feng and A. Li, The BP network learning algorithm based on particle swarm optimization, Journal of Computer Applications and Software, 20 (2003), 74-76. Google Scholar

[15]

V. Tandon, Closing the Gap between CAD/ CAM and Optimized CNC End Milling Indianapolis, USA: Purdue School of Engineering and Technology, Indiana University Purdue University, 2001. Google Scholar

[16]

D. Tsou and C. MacNish, Adaptive particle swarm optimization for high-dimensional highly convex search spaces, Proc. IEEE Int Conf on Intelligence Symposium. Canberra, 2 (2003), 783-798. doi: 10.1109/CEC.2003.1299747.  Google Scholar

[17]

Z. W. Wang, G. L. Durst and R. C. Eberhart, Particle Swarm Optimization and Neural Network Application for QSAR, Proc. IEEE Int. Conf. on Parallel and Distributed Processing Symposium, New Mexico, 2004. doi: 10.1109/IPDPS.2004.1303214.  Google Scholar

[18]

J. Wang, Q. Shen and H. Shen, et al., RBF neural network design based on the multiple population co-evolution particle swarm algorithm, Control Theory and Applications, 32 (2006), 251-255. Google Scholar

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