10.14489/td.2014.08.pp.075-079

2014, 08 August

DOI: 10.14489/td.2014.08.pp.075-079

Sedov A.V., Lipkin S.М., Lipkina М.S., Onyshko D.А.
ELECTROCHEMICAL EXPRESS-DIAGNOSTICS DEVICES FUNCTIONING AND DEVELOPING PRINCIPLES
(pp. 75-79)

Abstract. Functioning principles and basic structure of electrochemical express-diagnostics device are stated. Two-stage express-diagnostics algorithm is described and justifications of chosen dimension decrease transform (decomposing modelling method) and mass fraction calcu-lation (multilayer feed forward neural network) methods are given. Universalism of proposed express-diagnostics two-stage algorithm is shown on the examples of nickel, chromium, copper, carbon and manganese mass fraction identification.

Keywords: system of the non-destructive proximate analysis, metal alloys, chronopotentiometry, methods of dimension reduction of data, adaptive connectionist model, makeup of the mass concentration, generalization ability of model.

+ - About the Authors Click to collapse

A. V. Sedov, S. M. Lipkin, M. S. Lipkin, D. A. Onyshko
The Platov South Russia State Polytechnical University (NPI), Rostov-on-Don, Russia. E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

+ - References Click to collapse

1. Lipkin M. S., Kucherenko S. V., Tarasova N. V. et al. (2009). Possibilities of identification of carbon phase components in ironcarbonic alloys by electrochemical proximate analysis methods. Kontrol'. Diagnostika, (8), pp. 12-15.
2. Lipkin M. S., Kucherenko S. V., Lipkina T. V. et al. (2008). Non-destructive electrochemical determination of carbon in structural steels. Kontrol'. Diagnostika, (5), pp. 56-58.
3. Sedov A. V., Lipkin M. S., Onyshko D. A., Lipkin S. M. (2009). Mathematical modeling and identification of processes of electrochemical polarization in the system of express-analysis of metal alloys. Izvestiia Samarskogo nauchnogo tsentra RAN, 11(5), pp. 428-432.
4. Sedov A. V. (2010). Modeling objects with discrete and distributed parameters: a decomposition approach. Moscow: Nauka.
5. Lipkin M. S., Lipkina T. V., Lipkin S. M. (2008). Electrochemical sensor for the device of local electrochemi-cal express-analysis. Utility patent No. 74713. Russian Fed-eration.
6. Frumkin A. N. (Ed.). (1975). Introduction to elec-trochemical kinetics. Moscow: Vysshaia shkola.
7. Uossermen F. (1992). Neurocomputer engineering: theory and practice. Moscow: Mir.
8. Fukunaga K. (1979). Introduction to statistical theory of pattern recognition. Moscow: Nauka.
9. Kaufman L., Rousseeuw P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley & Sons, Inc.
10. Debok G., Kokhonen T. (2001). Analysis of finan-cial data by using self-organizing maps. Moscow: Al'pina Publisher.
11. Preparata F., Sheimos M. (1989). Computational geometry: an introduction. Moscow: Mir.
12. L'iung L. (1991). Identification systems. Theory for the user. Moscow: Nauka.
13. Terano T., Asai K., Sugeno M. (1993). Application fuzzy systems. Moscow: Mir.

+ - Purchase digital version of a single article Click to collapse

This article is available in electronic format (PDF).

DOI: 10.14489/td.2014.08.pp.075-079

Copy the article DOI and follow the link https://id-spektr.ru/product/pokupka-elektronnoy-stati-iz-zhurnala-kontrol-diagnostika

Please specify the article DOI in the order comments.