Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization

Chemometrics and Intelligent Laboratory Systems

Volumn 126, Issue , 2013, Pages 11-20

  Li, Guoqiang ^a,b   Niu, Peifeng ^a,b   Zhang, Weiping ^a,b   Liu, Yongchao ^a,b  

^a YANSHAN UNIVERSITY   (China)

^b NATIONAL ENGINEERING RESEARCH CENTER FOR EQUIPMENT AND TECHNOLOGY OF COLD STRIP ROLLING   (China)

Author keywords

Coal fired boiler; Least squares support vector machine; NOx emissions; Teaching learning based optimization

Indexed keywords

NITRIC OXIDE;

ACCURACY; ALGORITHM; AMELIORATED TEACHING LEARNING BASED OPTIMIZATION; ARTICLE; CONTROLLED STUDY; INTERMETHOD COMPARISON; LEAST SQUARE SUPPORT VECTOR MACHINE; MATHEMATICAL COMPUTING; NITROUS OXIDE EMISSION; PRIORITY JOURNAL; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84878200883     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.04.012     Document Type: Article

References (29)

1. Smrekar J., Assadi M., Fast M., Kuštrin I., De S. Development of artificial neural network model for a coal-fired boiler using real plant data. Energy 2009, 34(2):144-152.
2. Smrekar J., Pandit D., Fast M., Assadi M., De S. Prediction of power output of a coal-fired power plant by artificial neural network. Neural Computing and Applications 2010, 19(5):725-740.
3. Tanetsakunvatana V., Kuprianov V.I. Experimental study on effects of operating conditions and fuel quality on thermal efficiency and emission performance of a 300-MW boiler unit firing Thai lignite. Fuel Processing Technology 2007, 88(2):199-206.
4. Li G., Niu P., Liu C., Zhang W. Enhanced combination modeling method for combustion efficiency in coal-fired boilers. Applied Soft Computing 2012, 12:3132-3140.
5. Zhou H., Zheng L., Cen K. Computational intelligence approach for NOx emissions minimization in a coal-fired utility boiler. Energy Conversion and Management 2010, 51(3):580-586.
6. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9(3):293-300.
7. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York, NY.
8. Vapnik V. Statistical Learning Theory 1998, Wiley, New York.
9. Zhu Z., Zhu X., Guo Y., Ye Y., Xue X. Inverse matrix-free incremental proximal support vector machine. Decision Support Systems 2012, 10.1016/j.dss.2012.02.007.
10. Rao R.V., Savsani V.J., Vakharia D.P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer Aided Design 2011, 43(3):303-315.
11. Rao R.V., Savsani V.J., Vakharia D.P. Teaching-learning-based optimization: a novel optimization method for continuous non-linear large scale problems. Information Sciences 2012, 183(1):1-15.
12. Kennedy J., Eberhart R.C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks 1995, 4:1942-1948. IEEE, Piscataway, NJ, United States.
13. Alexouda G. A user-friendly marketing decision support system for the product line design using evolutionary algorithms. Decision Support Systems 2005, 38(4):495-509.
14. Wong M.L., Guo Y.Y. Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm. Decision Support Systems 2008, 45(2):368-383.
15. Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06 2005, Erciyes University, Kayseri, Turkey.
16. Karaboga D., Akay B. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 2009, 214(1):108-132.
17. Karaboga D., Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 2008, 8(1):687-697.
18. Li G., Niu P., Xiao X. Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied Soft Computing 2012, 12(1):320-332.
19. Li G., Niu P., Zhang W. Corrigendum to "Development and investigation of efficient artificial bee colony algorithm for numerical function optimization". Applied Soft Computing 2012, 12:320-332. 10.1016/j.asoc.2012.12.026.
20. Rashedi E., Nezamabadi-pour H., Saryazdi S. GSA: a gravitational search algorithm. Information Sciences 2009, 179(13):2232-2248.
21. Xavier de Souza S., Suykens J.A.K., Vandewalle J., Bolle D. Coupled simulated annealing. IEEE Transactions on Systems, Man, and Cybernetics Part B 2010, 40(2):320-335.
22. Rao R.V., Patel V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations 2012, 10.5267/j.ijiec.2012.03.007.
23. Rao R.V., Patel V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling 2012, 10.1016/j.apm.2012.03.043.
24. Satapathy S.C., Naik A. Data clustering based on teaching-learning-based optimization. LNCS 2011, 7077(2):148-156.
25. Goldberg D.E. Genetic Algorithms in Search Optimization and Machine Learning 1989, Addison-Wesley, Boston.
26. Holland J.H. Adaptation in Natural and Artificial Systems 1975, University of Michigan Press, Ann Arbor.
27. Pham D.T., Ghanbarzadeh A., Koç E., Otri S., Rahim S., Zaidi M. The Bees algorithm-a novel tool for complex optimisation problems. Proceedings of IPROMS 2006 Conference 2006, 454-461.
28. Ahrari A., Atai A.A. Grenade explosion method-a novel tool for optimization of multimodal functions. Applied Soft Computing 2010, 10(4):1132-1140.
29. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J. Least Squares Support Vector Machines 2002, World Scientific, Singapore.