Fuzzy Identification of Technological Objects

2015;
: pp. 35 – 42
https://doi.org/10.23939/jeecs2015.01.035
Received: March 19, 2015
Revised: April 15, 2015
Accepted: May 19, 2015
1
Ivano-Frankivsk National Technical University of Oil and Gas
2
Ivano-Frankivsk National Technical University of Oil and Gas
3
Ivano-Frankivsk National Technical University of Oil and Gas

In practice, information on statistical characteristics of series mode interference is available only in certain cases. Moreover, for this or that reason input variables are measured inexactly, and their values can be specified with some uncertainty. The identification task is significantly complicated when the measuring signal passes through a natural channel with unknown statistical characteristics. This is, for instance, the case with well drilling when axial weight on the drilling bit and drilling bit rotations per minute are gauged by above-ground devices. The study resulted in developing a method of building polynomial empirical models for a case when input factors are fuzzy variables with known Gaussian membership functions. The output variable of the model was shown to be also a fuzzy variable in such conditions; the corresponding membership function on the basis of which the identification task is formulated was obtained. To determine the parameters of the model with fuzzy input factors, the least square method was applied, which allowed obtaining a respective formula incorporating the information on input factors fuzziness.

  1. Ivakhnenko A. G. Simulation of interference stability: monograph / A. G. Ivakhnenko, V. S. Stepashko. – Kyiv : Naukova Dumka, 1985. – 216 p. (in Russian)
  2. Ivakhnenko A. G. Inductive method of self organization of complicated systems models : monograph / A. G. Ivakhnenko – Kyiv : Naukova Dumka, 1981. – 296 p. (in Russian)
  3. Ivakhnenko A. G. Handbook on typical programs for simulation / A. G. Ivakhnenko, Y. V. Koppa, V. S. Stepashko et al. ; under edition of A. G. Ivakhnenko – Kyiv : Tekhnika, 1980. – 180 p. (in Russian)
  4. Gorbiychuk M. I. Method of synthesis of empirical models based on the genetic algorithms / M. I. Gorbiychuk, M. I. Kohutyak, O. B. Vasylenko, I. V. Shchupak // Exploration and development of oil and gas fields. – 2009. – No 4 (33). – P. 72–79. (in Ukrainian)
  5. Gorbiychuk M. I. Method of synthesis of empirical models with taking into account the measurement errors / M. I. Gorbiychuk, I. V. Shchupak, T. Oskolip // Methods and devices of quality control. – 2011. – No 2 (27). – P. 67–76. (in Ukrainian)
  6. Demin D. A. Linear regression analysis of a small sample of fuzzy initial data / D. A. Demin, O. V. Seraya // International scientific and engineering journal “Problems of control and automatics” – 2012. – No 4. – P. 129–142. (in Russian)
  7. Gorbiychuk M. I. Inductive method of building the mathematical models of natural gas pumping facilities / M. I. Gorbiychuk, M. I. Kohutyak, Y. I. Zayachuk // Oil and gas industry. – 2008. – No 5. – P. 32–35. (in Ukrainian)
  8. Dubois D. The theory of possibilities. Appendix to knowledge representation in computer science; translated from French / D. Dubois, A. Prad – Moscow : Radio and Communications, 1990. – 286 p. (in Russian)
  9. Raskin L. G. Fuzzy mathematics. Basics of theory. Appendixes / L. G. Raskin, O. V. Seraya. – Kharkiv : Parus, 2008. – 352 p. (in Russian)
  10. Ermakov S. M. Mathematical theory of optimal experiment / S. M. Ermakov, A. A. Zhiglavskiy – Moscow : Nauka, 1987. – 280 p. (in Russian)
M. Horbiychuk, T. Humeniuk, D. Povarchuk. Fuzzy Identification of Technological Objects. Energy Eng. Control Syst., 2015, Vol. 1, No. 1, pp. 35 – 42. https://doi.org/10.23939/jeecs2015.01.035