Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm

Neurocomputing

Volumn 119, Issue , 2013, Pages 469-477

  Jiang, Huaqin ^a   Yan, Zhengbing ^a   Liu, Xinggao ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Ant colony optimization; Immune clone particle swarm optimization; Industrial polypropylene manufacture; Least squares support vector machines; Melt index prediction

Indexed keywords

ANT COLONY OPTIMIZATION (ACO); GLOBAL SEARCH CAPABILITY; HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM; IMMUNE CLONE PARTICLE SWARM OPTIMIZATIONS; LEAST SQUARES SUPPORT VECTOR MACHINES; MELT INDEX PREDICTIONS; PRE-MATURE CONVERGENCES; PROPYLENE POLYMERIZATION;

ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; CLONING; FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); POLYPROPYLENES;

ALGORITHMS;

ACCURACY; ALGORITHM; ANT COLONY IMMUNE CLONE PARTICLE SWARM OPTIMIZATION; ARTICLE; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEAST SQUARES SUPPORT VECTOR MACHINE; MELT INDEX PREDICTION; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; VALIDITY;

EID: 84881550877     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.03.006     Document Type: Article

References (40)

1. Bafna S.S., Beall A.M. A design of experiments study on the factors affecting variability in the melt index measurement. J. Appl. Polym. Sci. 1997, 65(2):277-288.
2. Ahmed F., Nazir S., Yeo Y.K. A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant. J. Chem. Eng. 2009, 26(1):14-20.
3. Xie L., Liu Y.J., Yang H.Z., Ding F. Modeling and identification for non-uniformly periodically sampled-data systems. IET Control Theory Appl. 2010, 4(5):784-794.
4. Chen Y., Liu X.G. Modeling mass transport of propylene polymerization on Ziegler-Natta catalyst. Polym. J. 2005, 46:9434-9442.
5. R.K. Al Seyab, Y. Cao, S. Yang, A. Case Study of Predictive Contorl for the Alstom Gasifier Problem, in: Proceedings of UK Control, 2004.
6. Lee E.H., Kim T.Y., Yeo Y.K. Prediction of the melt index in a high-density polyethylene process. J. Chem. Eng. Jpn. 2007, 40(10):840-846.
7. Luo Z.H., Wen S.H., Zheng Z.W. Modeling the effect of polymerization rate on the intraparticle mass and heat transfer during propylene polymerization in a loop reactor. J. Chem. Eng. Jpn. 2009, 42(8):576-580.
8. Embiruçu M., Lima E.L., Pinto J.C. Continuous soluble Ziegler-Natta ethylene polymerizations in reactor trains. I. Mathematical modeling. J. Appl. Polym. Sci. 2000, 77(7):1574-1590.
9. Tian C.Q., Liao Y.F., Li X.T. A mathematical model of variable displacement swash plate compressor for automotive air conditioning system. Int. J. Refrig. 2006, 29(2):270-280.
10. B. Feil, J. Abonyi, P. Pach, S. Nemeth, P. Arva, M. Nemeth, G. Nagy, Semi-mechanistic models for state-estimation-Soft sensor for polymer melt index prediction, in: Proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, 2004, pp. 1111-1117.
11. Peng C., Tian Y.C., Tade M.O. State feedback controller design of networked control systems with interval time-varying delay and nonlinearity. Int. J. Robust Nonlinear Control 2008, 18(12):1285-1301.
12. Xiong Z.H., Xu Y.X., Zhang J., Dong J. Batch-to-batch control of fed-batch processes using control-affine feedforward neural network. Neural. Comput. Appl. 2008, 17(4):425-432.
13. Virkki-Hatakka T., Bulsari A. Scalability of neural network models makes industrial use easier. Pap. Puu 2002, 84(7):463-466.
14. Lu C., Shi B.X., Chen L. An expandable on-chip BP learning neural network chip. Int. J. Electron. 2003, 90(5):331-340.
15. Shi J., Liu X.G., Sun Y.X. Melt index prediction by neural networks based on independent component analysis and multi-scale analysis. Neurocomputing 2006, 70:280-287.
16. Shi J., Liu X.G. Melt index prediction by neural soft-sensor based on multi-scale analysis and principal component analysis. Chin. J. Chem. Eng 2005, 13(6):849-852.
17. Zhang J., Jin Q.B., Xu Y.M. Inferential estimation of polymer melt index using sequentially trained bootstrap aggregated neural networks. Chem. Eng. Technol. 2006, 29(4):442-448.
18. Han I.S., Han C., Chung C.B. Melt index modeling with support vector machines, partial least squares, artificial neural networks. J. Appl. Polym. Sci. 2005, 95(4):967-974.
19. Shi J., Liu X.G. Melt index prediction by weighted least squares support vector machines. J. Appl. Polym. Sci. 2006, 101(1):285-289.
20. Marinakis Y., Marinaki M. A hybrid genetic-Particle Swarm Optimization Algorithm for the vehicle routing problem. Expert. Syst. Appl. 2010, 37(2):1446-1455.
21. Yagiz S., Karahan H. Prediction of hard rock TBM penetration rate using particle swarm optimization. Int. J. Rock. Mech. Min. 2011, 48(3):427-433.
22. Lin C.J., Hong S.J. The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition. Neurocomputing 2007, 71(1-3):297-310.
23. Onüt S., Tuzkaya U.R., Dogaç B. A particle swarm optimization algorithm for the multiple-level warehouse layout design problem. Comput. Ind. Eng 2008, 54(4):783-799.
24. Chen G.L., Guo W.Z., Chen Y.Z. A PSO-based intelligent decision algorithm for VLSI floorplanning. Soft Comput. 2010, 14(12):1329-1337.
25. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer, New York.
26. Vapnik V.N. Statistical Learning Theory 1998, John Wiley, New York.
27. Shin K.S., Lee T.S., Kim H.J. An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl. 2005, 28(1):127-135.
28. Yang J.L., Li H.X., Hu Y. A probabilistic SVM based decision system for pain diagnosis. Expert Syst. Appl. 2011, 38(8):9346-9351.
29. Wang B., Wan F. Classification of single-trial EEG based on support vector clustering during finger movement. Lect. Notes. Comput. Sci. 2009, 5552:354-363.
30. Suykens J.A.K., Vandewalle J. Least squares support vector machines classifiers. Neural Process. Lett. 1999, 9(3):293-300.
31. Yang X.W., Lu J., Zhang G.Q. Adaptive pruning algorithm for least squares support vector machine classifier. Soft Comput. 2010, 14(7):667-680.
32. A. Colorni, M. Dorigo, V. Maniezzo, Positive Feedback as a Search Strategy, Politecnico di Milano, Italy, Technical Report no. 91-016 1991.
33. A. Colorni, M. Dorigo, V. Maniezzo The Ant System: an Autocatatlytic Process, Politecnico di Milano, Italy, Technical Report no. 91-016, 1991.
34. Dorigo M., Maniezzo V., Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 1996, 26(1).
35. J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks Conference, 4, 1995, pp. 1942-1948.
36. Yuan X.H., Su A.J., Nie H., Yuan Y.B., Wang L. Unit commitment problem using enhanced particle swarm optimization algorithm. Soft Comput. 2011, 15:139-148.
37. Che Z.H. PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Comput. Ind. Eng. 2010, 58(4):625-637.
38. Modares H., Alfi A., Naghibi S., Mohammad B. Parameter estimation of bilinear systems based on an adaptive particle swarm optimization. Eng. Appl. Artif. Intell. 2010, 23(7):1105-1111.
39. Zhan Z.H., Zhang J., Li Y., Chung H.S.H. Adaptive particle swarm optimization. IEEE Trans. Syst. Man. Cybern. Part. B. Cybern. 2009, 39(6):1362-1381.
40. Murray-Smith D.J. Methods for external validation of continuous system simulation models: a review. Math. Compt. Model. Dyn. Syst. 1998, 4(1):5-31.