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Prediction method based on optimization theory and its application
1. | School of Economics and Management, Beihang University, Beijing 100191, China, China |
2. | School of Reliability and System Engineering, Beihang University, Beijing 100191, China |
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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