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Intelligent control model and its simulation of flue temperature in coke oven
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. 
[2] 
C. A. C. Coello, et al., Use of particle swarm optimization to design combinational logic circuits,, Lecture Notes in Computer Science (S03029743), 2606 (2003), 0302. doi: 10.1007/3540365532_36. 
[3] 
K. W. Chau, Application of a PSObased neural network in analysis of outcomes of construction claims,, Automation in Construction (S09265805), 16 (2007), 0926. doi: 10.1016/j.autcon.2006.11.008. 
[4] 
F. van den Bergh, Particle Swarm Weight Initialization in Multilayer Neural Networks,, Development and Pracrice of Artificial Intelligence Techniques, (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. 
[6] 
D. K. Grafiotis and W. Cedeno, Feature selection for structureactivity correlation using binary particle swarm,, Med. Chem., 45 (2002), 1098. doi: 10.1021/jm0104668. 
[7] 
D. Gies and R. Yahya, Particle swarm optimization for reconfigurable phase differentiated array design,, Microwave and Optical Technology Letters (S08952477), 38 (2003), 0895. 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. 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, (1998), 74. 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. 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 (S09574174), 34 (2008), 0957. 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, (2001), 445. doi: 10.1007/9781461302797_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. 
[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. 
[15] 
V. Tandon, Closing the Gap between CAD/ CAM and Optimized CNC End Milling Indianapolis,, USA: Purdue School of Engineering and Technology, (2001). 
[16] 
D. Tsou and C. MacNish, Adaptive particle swarm optimization for highdimensional highly convex search spaces,, Proc. IEEE Int Conf on Intelligence Symposium. Canberra, 2 (2003), 783. 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, (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 coevolution particle swarm algorithm,, Control Theory and Applications, 32 (2006), 251. 
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. 
[2] 
C. A. C. Coello, et al., Use of particle swarm optimization to design combinational logic circuits,, Lecture Notes in Computer Science (S03029743), 2606 (2003), 0302. doi: 10.1007/3540365532_36. 
[3] 
K. W. Chau, Application of a PSObased neural network in analysis of outcomes of construction claims,, Automation in Construction (S09265805), 16 (2007), 0926. doi: 10.1016/j.autcon.2006.11.008. 
[4] 
F. van den Bergh, Particle Swarm Weight Initialization in Multilayer Neural Networks,, Development and Pracrice of Artificial Intelligence Techniques, (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. 
[6] 
D. K. Grafiotis and W. Cedeno, Feature selection for structureactivity correlation using binary particle swarm,, Med. Chem., 45 (2002), 1098. doi: 10.1021/jm0104668. 
[7] 
D. Gies and R. Yahya, Particle swarm optimization for reconfigurable phase differentiated array design,, Microwave and Optical Technology Letters (S08952477), 38 (2003), 0895. 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. 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, (1998), 74. 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. 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 (S09574174), 34 (2008), 0957. 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, (2001), 445. doi: 10.1007/9781461302797_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. 
[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. 
[15] 
V. Tandon, Closing the Gap between CAD/ CAM and Optimized CNC End Milling Indianapolis,, USA: Purdue School of Engineering and Technology, (2001). 
[16] 
D. Tsou and C. MacNish, Adaptive particle swarm optimization for highdimensional highly convex search spaces,, Proc. IEEE Int Conf on Intelligence Symposium. Canberra, 2 (2003), 783. 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, (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 coevolution particle swarm algorithm,, Control Theory and Applications, 32 (2006), 251. 
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