Prediction method based on optimization theory and its application

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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

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

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

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