РЕШЕНИЕ ОБРАТНЫХ ЗАДАЧ СПЕКТРОСКОПИИ КОМБИНАЦИОННОГО РАССЕЯНИЯ ВОДНЫХ РАСТВОРОВ СОЛЕЙ С ПРИМЕНЕНИЕМ ВЕЙВЛЕТ-НЕЙРОННЫХ СЕТЕЙ
https://doi.org/10.25205/2541-9447-2018-13-3-101-109
Аннотация
Об авторах
С. А. БуриковРоссия
А. О. Ефиторов
Россия
Т. А. Доленко
Россия
В. Р. Широкий
Россия
С. А. Доленко
Россия
Список литературы
1. Шевцов М. Н. Водно-экологические проблемы и использование водных ресурсов. Хабаровск: Изд-во Тихоокеан. гос. ун-та, 2015. 197 с. ISBN 978-5-7389-1817-9.
2. Andersson M., Edner H., Johansson J., Svanberg S., Wallinder E., Weibring P. Remote sensing of the environment using laser radar techniques // Atomic Physics Methods in Modern Research. 1997. Vol. 499. P. 257-269. Springer. DOI: 10.1007/BFb0104329.
3. Harsdorf S., Janssen M., Reuter R., Toeneboen S., Wachowicz B., Willkomm R. Submarine lidar for seafloor inspection // Meas Sci Tech. 1999. Vol. 10. No. 12. P. 1178-1184.
4. Dolenko T. A., Churina I. V., Fadeev V. V., Glushkov S. M. Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media // J. Raman Spectroscopy. 2000. Vol. 31. P. 863- 870.
5. Kauffmann T. H., Fontana M. D. Inorganic salts diluted in water probed by Raman spectrometry: Data processing and performance evaluation // Sensors and Actuators B. 2015. Vol. 209. P. 154-161.
6. Rudolph W. W., Irmer G. Raman and Infrared Spectroscopic Investigation on Aqueous Alkali Metal Phosphate Solutions and Density Functional Theory Calculations of Phosphate-Water Clusters // Appl. Spectroscopy. 2007. Vol. 61. No. 12. P. 274A-292A.
7. Rull F., De Saja J. A. Effect of electrolyte concentration on the Raman spectra of water in aqueous solutions // J. Raman Spectroscopy. 1986. Vol. 17. No. 2. P. 167-172.
8. Sadate S., Kassu A., Farley C. W., Sharma A., Hardisty J., Lifson Miles T. K. Standoff Raman measurement of nitrates in water // Proc. SPIE Remote Sensing and Modeling of Ecosystems for Sustainability VIII. 2011. P. 81560D-1-6.
9. Somekawa T., Tani A., Fujita M. Remote Detection and Identification of CO2 Dissolved in Water Using a Raman Lidar System // Applied Physics Express. 2011. Vol. 4. P. 1124011-3.
10. Mernagh T. P., Wilde A. R. The use of the laser Raman microprobe for the determination of salinity in fluid inclusions // Geochimica et Cosmochimica Acta. 1989. Vol. 53. P. 765- 771.
11. Burikov S. A., Dolenko S. A., Dolenko T. A., Persiantsev I. G. Application of Artificial Neural Networks to Solve Problems of Identification and Determination of Concentration of Salts in Multi-Component Water Solutions by Raman Spectra // Optical Memory and Neural Networks (Information Optics). 2010. Vol. 19. No. 2. P. 140-148.
12. Efitorov A., Burikov S., Dolenko T., Laptinskiy K., Dolenko S. Significant Feature Selection in Neural Network Solution of an Inverse Problem in Spectroscopy // Procedia Computer Science. 2015. Vol. 66. P. 93-102.
13. Haykin S. S. Neural networks and learning machines. 3rd ed. Upper Saddle River. NJ. USA: Pearson, 2009.
14. Esbensen K. H. Multivariate Data Analysis // Practice, an Introduction to Multivariate Data Analysis and Experimental Design. 5th ed., CAMO Software AS. 2006, 599 p.
15. Rumondor A. C., Taylor L. S. Application of partial least-squares (PLS) modeling in quantifying drug crystallinity in amorphous solid dispersions // Int. J. Pharm. 2010. Vol. 398. Is. 1-2. P. 155-160.
16. Gushchin K. A., Burikov S. A., Dolen- ko T. A., Persiantsev I. G., Dolenko S. A. Data dimensionality reduction and evaluation of clusterization quality in the problems of analysis of composition of multi-component solutions // Optical Memory and Neural Networks. 2015. Vol. 24. Is. 3. P. 218-224.
17. Kamruzzaman S. M., Ahmed Ryadh Hasan. Pattern Classification using Simplified Neural Networks. arXiv:1009.4983v1 [cs.NE]
18. Peng H., Ding C., Long F. Minimum redundancy maximum relevance feature selection // IEEE Intelligent Systems. 2005. Vol. 20. No. 6. P. 70-71
19. Dolenko S., Burikov S., Dolenko T., Efitorov A., Gushchin K., Persiantsev I. Neural Network Approaches to Solution of the Inverse Problem of Identification and Determination of Partial Concentrations of Salts in Multiсomponent Water Solutions // Lecture Notes in Computer Science. 2014. Vol. 8681. P. 805-
20. Efitorov A., Dolenko T., Burikov S., Laptinskiy K., Dolenko S. Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts by Artificial Neural Networks // Lecture Notes in Computer Science. 2016. Vol. 9887. P. 355
21. Strang G., Nguyen T. Wavelets and filter banks. 2nd ed. Wellesley-Cambridge Press, 1996. 520 p.
22. Mallat S. G. A theory for multiresolution signal decomposition: the wavelet representation // IEEE Transactions on Pattern Recognition and Machine Intelligence. 1989. Vol. 11. No. 7. P. 674-693.
23. Daubechies I. Orthonormal bases of compactly supported wavelets // Comm. Pure & Appl. Math. 1988. Vol. 41. No. 7. P. 909- 996.
24. Jean-Philippe Lachaux, Antoine Lutz, David Rudrauf, Diego Cosmelli, Michel Le Van Quyen, Jacques Martinerie, Francisco Varela. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence // Neurophysiol Clin. Elsevier. 2002. Vol. 32. P. 157-174.
25. Daubechies I. Ten Lectures on Wavelets // SIAM. 1992. 356 p.
26. Efitorov A., Dolenko T., Burikov S., Laptinskiy K., Dolenko S. Neural Network Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts // Advances in Intelligent Systems and Computing. 2016. Vol. 449. P. 273-279.
Рецензия
Для цитирования:
Буриков С.А., Ефиторов А.О., Доленко Т.А., Широкий В.Р., Доленко С.А. РЕШЕНИЕ ОБРАТНЫХ ЗАДАЧ СПЕКТРОСКОПИИ КОМБИНАЦИОННОГО РАССЕЯНИЯ ВОДНЫХ РАСТВОРОВ СОЛЕЙ С ПРИМЕНЕНИЕМ ВЕЙВЛЕТ-НЕЙРОННЫХ СЕТЕЙ. Сибирский физический журнал. 2018;13(3):101-109. https://doi.org/10.25205/2541-9447-2018-13-3-101-109
For citation:
Burikov S.A., Efitorov A.O., Dolenko T.A., Shirokiy V.R., Dolenko S.A. SOLUTION OF INVERSE PROBLEMS OF RAMAN SPECTROSCOPY OF AQUEOUS SALT SOLUTIONS WITH THE APPLICATION OF WAVELET NEURAL NETWORKS. SIBERIAN JOURNAL OF PHYSICS. 2018;13(3):101-109. (In Russ.) https://doi.org/10.25205/2541-9447-2018-13-3-101-109