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Universal technique for optimization of neural network training parameters: gasoline near infrared data example

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Abstract

The universal technique of finding optimum training parameters for multi-layer perceptron—such as percentage of samples in a cross-validation set and quantities of training iterations with various initial values—is offered. This technique is aimed at the searching of optimum values of two complex factors depending on accuracy and convergence of a network, and also on the time of its training. Their conventional names are “cross-validation coefficient” and “training iteration coefficient”. Near infrared spectroscopy data for gasoline samples are used to evaluate the efficiency of the method.

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References

  1. Côcco LC, Yamamotob CI, von Meien OF (2005) Study of correlations for physicochemical properties of Brazilian gasoline. Chemom Intell Lab Syst 76:55–63. doi:10.1016/j.chemolab.2004.09.004

    Article  Google Scholar 

  2. van Leeuwen JA, Jonker RJ, Gill R (1994) Octane number prediction based on gas chromatographic analysis with non-linear regression techniques. Chemom Intell Lab Syst 25:325–390. doi:10.1016/0169-7439(94)85051-8

    Article  Google Scholar 

  3. Andrade JM, Sánchez MS, Sarabia LA (1999) Applicability of high-absorbance MIR spectroscopy in industrial quality control of reformed gasolines. Chemom Intell Lab Syst 46:41–55. doi:10.1016/S0169-7439(98)00156-7

    Article  Google Scholar 

  4. Yang H, Ring Z, Briker Y, McLean N, Friesen W, Fairbridge C (2002) Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS. Fuel 81:65–74. doi:10.1016/S0016-2361(01)00121-1

    Article  Google Scholar 

  5. Meusingera R, Morosb R (2001) Determination of octane numbers of gasoline compounds from their chemical structure by 13C NMR spectroscopy and neural networks. Fuel 80:613–621. doi:10.1016/S0016-2361(00)00125-3

    Article  Google Scholar 

  6. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart D, McClelland J (eds) Parallel data processing, vol I, Chap. 8. The MIT Press, Cambridge, pp 318–362

  7. Wang W, Paliwal J (2006) Generalisation performance of artificial neural networks for near infrared spectral analysis. Biosyst Eng 94:7–18. doi:10.1016/j.biosystemseng.2006.02.001

    Article  Google Scholar 

  8. Burns JA, Whitesides GM (1993) Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition. Chem Rev 93:2583–2601. doi:10.1021/cr00024a001

    Article  Google Scholar 

  9. Balabin RM, Safieva RZ, Lomakina EI (2008) Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra. Chemometr Intell Lab 93:58. doi:10.1016/j.chemolab.2008.04.003

    Article  Google Scholar 

  10. Balabin RM, Safieva RZ (2008) Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data. Fuel 87:2745. doi:10.1016/j.fuel.2008.02.014

  11. Balabin RM, Safieva RZ (2007) Capabilities of near infrared spectroscopy for the determination of petroleum macromolecule content in aromatic solutions. J Near Infrared 15(Spec):343. doi:10.1255/jnirs.749

    Article  Google Scholar 

  12. Russian Standard: GOST R 51069-97

  13. Moody J, Utans J (1992) Principled architecture selection for neural networks: application to corporate bond rating prediction. In: Moody J, Hanson SJ, Lippmann RP (eds) Advances in neural information processing systems, vol IV. Morgan Kaufmann, San Mateo, pp 683–690

  14. Larsen J, Goutte C (1999) On optimal data split for generalisation estimation and model selection neural networks for signal processing. In: IX Proceedings of the 1999 IEEE signal processing society workshop, pp 225–234

  15. Jurs PC, Bakken GA, McClelland HE (2000) Computational methods for the analysis of chemical sensor array data from volatile analytes. Chem Rev 100:2649–2678. doi:10.1021/cr9800964

    Article  Google Scholar 

  16. Balabin RM, Syunyaev RZ (2008) Petroleum resins adsorption onto quartz sand: near infrared (NIR) spectroscopy study. J Colloid Interface Sci 318:167. doi:10.1016/j.jcis.2007.10.045

    Article  Google Scholar 

  17. Balabin RM, Safieva RZ (2008) Gasoline classification by source and type based on near infrared (NIR) spectroscopy data. Fuel 87:1096. doi:10.1016/j.fuel.2007.07.018

    Article  Google Scholar 

  18. Balabin RM, Safieva RZ, Lomakina EI (2007) Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. Chemometr Intell Lab 88:183. doi:10.1016/j.chemolab.2007.04.006

    Article  Google Scholar 

  19. Balabin RM, Syunyaev RZ, Karpov SA (2007) Molar enthalpy of vaporization of ethanol–gasoline mixtures and their colloid state. Fuel 86:323. doi:10.1016/j.fuel.2006.08.008

    Article  Google Scholar 

  20. Balabin RM (2008) Dispersed structure of ethanol–gasoline fuel according to dynamic light scattering method. J Dispersion Sci Tech 29:457. doi:10.1080/01932690701718925

    Article  Google Scholar 

  21. Balabin RM, Syunyaev RZ, Karpov SA (2007) Quantitative measurement of ethanol distribution over fractions of ethanol-gasoline fuel. Energy Fuels 21:2460. doi:10.1021/ef070081l

    Article  Google Scholar 

  22. Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Oxford University Press, New York

    Google Scholar 

  23. Balabin RM (2008) Intermolecular dispersion interactions of normal alkanes with rare gas atoms: van der Waals complexes of n-pentane with helium, neon, and argon. Chem Phys 352:267. doi:10.1016/j.chemphys.2008.06.015

    Article  Google Scholar 

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Acknowledgments

Balabin Roman is grateful to the ITERA International Group of companies for a nominal scholarship. Authors are grateful to Lumex Ltd. R&D and Production Company (and personally to Demygin SS and Chulyukov OG) for supplying the InfraLUM FT-10 FT-NIR Analyzer.

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Authors and Affiliations

  1. Gubkin Russian State University of Oil and Gas, 119991, Moscow, Russia

    Roman M. Balabin & Ravilya Z. Safieva

  2. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119992, Moscow, Russia

    Ekaterina I. Lomakina

Authors
  1. Roman M. Balabin
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  2. Ravilya Z. Safieva
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  3. Ekaterina I. Lomakina
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Correspondence to Roman M. Balabin.

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Balabin, R.M., Safieva, R.Z. & Lomakina, E.I. Universal technique for optimization of neural network training parameters: gasoline near infrared data example. Neural Comput & Applic 18, 557–565 (2009). https://doi.org/10.1007/s00521-008-0213-3

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  • DOI: https://doi.org/10.1007/s00521-008-0213-3

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