Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process

Applied Mathematics and Computation

Volumn 227, Issue , 2014, Pages 164-170

  Sanchez Lasheras F ^a   Garcia Nieto P J ^a   De Cos Juez, F J ^a   Vilan Vilan J A ^b  

^a UNIVERSITY OF OVIEDO   (Spain)

^b UNIVERSITY OF VIGO   (Spain)

Author keywords

Evolutionary algorithms (EAs); Evolutionary support vector machines (ESVMs); Hard chromium plating process; Machine learning; Support vector machines for regression (SVR)

Indexed keywords

ELECTROPLATING INDUSTRY; ELECTROPLATING PROCESS; EVOLUTIONARY ALGORITHMS (EAS); EVOLUTIONARY STRATEGIES; EVOLUTIONARY SUPPORT VECTOR MACHINES (ESVMS); HARD CHROMIUM PLATING; SUPPORT VECTOR MACHINE (SVMS); SUPPORT VECTOR REGRESSION ALGORITHMS;

CHROMIUM PLATING; COATINGS; CORROSION RESISTANCE; CRACKS; DEPOSITS; DESIGN OF EXPERIMENTS; ELECTROPLATING SHOPS; EVOLUTIONARY ALGORITHMS; HARDNESS; LEARNING SYSTEMS; NEURAL NETWORKS; NICKEL COATINGS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; WEAR RESISTANCE;

CHROMIUM;

EID: 84889642598     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2013.11.031     Document Type: Article

References (33)

1. C.G. Fink, U.S. Patent 1,581,188, April 20, 1926.
2. G.J. Sargent Electrolytic chromium Transactions of the American Electrochemical Society 37 1920 479 497
3. M.J. Udy Chromium 1956 Reinhold Publishing Corporation New York
4. J.K. Dennis, and T.E. Such The Nickel and Chromium Plating 1994 Woodhead Publishing New York
5. R.K. Guffie The Handbook of Chromium Plating 1986 Gardner Publications Ltd New York
6. J.H. Lindsay Decorative and Hard Chromium Plating Plating and Surface Finishing 90 2003 22 24
7. M. Schlesinger, and M. Paunovich Modern Electroplating 2000 Wiley-Interscience New York
8. F. Sánchez Lasheras, J.A. Vilán Vilán, P.J. García Nieto, and J.J. del Coz Díaz The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process Mathematical and Computer Modelling 52 7-8 2010 1169 1176
9. T.H. Irvine The Chemical Analysis of Electroplating Solutions 2000 Chemical Publishing Company New York
10. N. Kanani Electroplating: Basic Principles, Processes and Practice 2005 Elsevier Science Amsterdam
11. T. Ölmez The optimization of Cr (VI) reduction and removal by electrocoagulation using response surface methodology Journal of Hazardous Materials 162 2009 1371 1378
12. M.J. Ortiz Effect of a thin hard chromium coating on fatigue behavior of 4140 steel Surface Engineering 20 2004 345 352
13. A.P. Engelbrecht Computational Intelligence: An Introduction 2007 Wiley & Sons River Street, Hoboken, New Jersey
14. F. Friedrichs, and C. Igel Evolutionary tuning of multiple SVM parameters Neurocomputing 64 2005 107 117 (Pubitemid 40425314)
15. H.B. Kazemian, K. White, and D. Palmer-Brown Applications of evolutionary SVM to prediction of membrane alpha-helices Expert Systems with Applications 40 9 2013 3412 3420
16. V. Vapnik Statistical Learning Theory 1998 Wiley-Interscience New York
17. C. Cortes, and V. Vapnik Support vector networks Machine Learning 20 3 1995 273 297
18. A.J. Smola, and B. Schölkopf A tutorial on support vector regression Statistics and Computing 14 3 1998 199 222
19. J.C. Álvarez Antón, P.J. García Nieto, F.J. de Cos Juez, F. Sánchez Lasheras, M. González Vega, and M.N. Roqueñí Gutiérrez Battery state-of-charge estimator using the SVM technique Applied Mathematical Modelling 37 9 2013 6244 6253
20. V. Vapnik An overview of statistical learning theory IEEE Transactions on Neural Networks 10 1999 988 999
21. J. Shawe-Taylor, and N. Cristianini Kernel Methods for Pattern Analysis 2004 Cambridge University Press New York
22. I. Steinwart, and A. Christmann Support Vector Machines 2008 Springer New York
23. E. Alpaydin Introduction to Machine Learning 2004 The MIT Press New York
24. D.E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning 1989 Addison-Wesley Reading, MA
25. T. Bäck, G. Rudolph, H.P. Schwefel, Evolutionary programming and evolution strategies: similarities and differences, in: IEEE Computer Society (Eds.), Proceedings of the Second Annual Conference on Evolutionary Programming, Washington, DC, USA, 1993, pp. 11-22.
26. D.B. Fogel An introduction to simulated evolutionary optimization IEEE Transactions on Neural Networks 5 1 1994 3 14
27. X. Yao, Y. Liu, and G. Lin Evolutionary programming made faster IEEE Transactions on Evolutionary Computation 3 2 1999 82 102
28. C.Y. Lee, and X. Yao Evolutionary programming using mutations based on the levy probability distribution IEEE Transactions on Evolutionary Computation 8 1 2004 1 13
29. S. Salcedo-Sanz, E.G. Ortiz-García, A.M. Pérez-Bellido, and A. Portilla-Figueras Short term wind speed prediction based on evolutionary support vector regression algorithms Expert Systems with Applications 38 2011 4052 4057
30. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
31. E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, A. Weingessel, e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, R package version 1.5-24, 2010.
32. R Development Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2011. ISBN 3-900051-07-0, URL: http://www.R-project.org/.
33. J.M. Chambers Software for Data Analysis: Programming with R 2008 Springer New York