NEURAL-BASED RECOGNITION OF MULTI-PARAMETER SENSOR SIGNAL DESCRIBED BY MATHEMATICAL MODEL

Authors

  • Iryna Turchenko
  • Volodymyr Kochan
  • Anatoly Sachenko

DOI:

https://doi.org/10.47839/ijc.3.2.298

Keywords:

multi-parameter sensor, sensor signal recognition, neural networks

Abstract

The possibility of artificial neural network usage for recognition of a signal of a multi-parameter sensor is described in this paper. The general structure of data acquisition channel with usage of neural networks as well as mathematical model of output signal of a multi-parameter sensor is studied in this article. The model of neural network, training algorithm and achieved results of simulation modeling of a multi-parameter sensor signal recognition using MATLAB software are presented at the end of this paper.

References

G. Samsonov, A. Kits, O. Kuzdeni, Sensors for temperature measurement in industry, Kiev: Naukova dumka, 1977, 223 p.

Tuz Yu, Structure methods of instrumentation accuracy improvement, Kiev: Vyshcha shkola, 1976, 255 p.

http://www.bibl.liu.se/liupubl/disp/disp99/tek573s.htm

J. Zakrzewski, W. Domanski, P. Chaitas and Th. Laopoulos, "Improving Sensitivity and Selectivity of SnO2 Gas Sensors by Temperature Variation", Proceedings of the second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003, Lviv, Ukraine, pp. 296-299.

Shaffer R. E., Rose-Pehrsson S. L., McGill A.R. A comparison study of chemical sensor array pattern recognition algorithms // Analytica Chimica Acta. – 1999. – Vol. 384, No. 3. – P. 305-317.

Daqi G., Shuyan W., Yan J. An electronic nose and modular radial basis function network classifiers for recognizing multiple fragrant materials // Sensors and Actuators B. – 2004. – Vol. 97, No. 2-3. – P. 391-401.

Zhang H., Balaban M., Principe J. Improving pattern recognition of electronic nose data with time-delay neural networks // Sensors and Actuators B. – 2003. – Vol. 96, No. 1-2. – P. 385-389.

Llobet E., Brezmes J., Gualdron O., Vilanova X., Correig X. Building parsimonious fuzzy ARTMAP models by variable selection with a cascaded genetic algorithm: application to multisensor systems for gas analysis // Sensors and Actuators B. – 2004. – Vol. 99, No. 2-3. – P. 267-272.

Ortega A., Marco S., Perera A., Sundic T., Pardo A., Samitier J. An intelligent detector based on temperature modulation of a gas sensor with a digital signal processor // Sensors and Actuators B. – 2001. – Vol. 78, No. 1-3. – P. 32-39.

Luo D., Hosseini G., Stewart J. Application of ANN with extracted parameters from an electronic nose in cigarette brand identification // Sensors and Actuators B. – 2004. – Vol. 99, No. 2-3. – P. 253-257.

Martin M., Santos J., Agapito J. Application of artificial neural networks to calculate the partial gas concentrations in a mixture // Sensors and Actuators B. – 2001. – Vol. 77, No. 1-2. – P. 468-471.

Srivastava A.K. Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network // Sensors and Actuators B. – 2003. – Vol. 96, No. 1-2. – P. 24-37.

Guo D., Wang Y., Nan C., Li L., Xia J. Application of artificial neural network technique to the formulation design of dielectric ceramics // Sensors and Actuators A. – 2002. – Vol. 102, No. 1-2. – P. 93-98.

Capone S., Siciliano P., Barsan N., Weimar U., Vasanelli L. Analysis of CO and CH4 gas mixtures by using a micromachined sensor array // Sensors and Actuators B: Chemical. – 2001. – Vol. 78, No. 1-3. – P. 40-48.

Hahn S., Barsan N., Weimar U. Investigation of CO/CH4 mixture measured with differently doped SnO2 sensors // Sensors and Actuators B. – 2001. – Vol. 78, No. 1-3. – P. 64-68.

V. Turchenko, V. Kochan, A. Sachenko, "Estimation of Computational Complexity of Sensor Accuracy Improvement Algorithm Based on Neural Networks", Lecture Notes in Computing Science, LNCS 2130, Ed. G. Dorffner, H. Bischof, and K. Hornik, Springer-Verlag, Berlin, Heidelberg, New York, 2001, pp. 743-748.

A. K. Jain, J. Mao, K. M. Mohiuddin, "Artificial neural networks: a tutorial", Computer, 1996, pp. 31-44.

M. T. Hagan and M. Menhaj, "Training feed-forward networks with the Marquardt algorithm", IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994

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Published

2014-08-01

How to Cite

Turchenko, I., Kochan, V., & Sachenko, A. (2014). NEURAL-BASED RECOGNITION OF MULTI-PARAMETER SENSOR SIGNAL DESCRIBED BY MATHEMATICAL MODEL. International Journal of Computing, 3(2), 140-147. https://doi.org/10.47839/ijc.3.2.298

Issue

2004, Volume 3, Issue 2

Section

Articles

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