THE IMPLEMENTATION OF FORMAL METHODS FOR INTELLIGENT CONTROL BASED ON FUZZY KNOWLEDGE ABOUT THE OIL AND GAS OBJECTS

Authors

  • М. М. Демчина ІФНТУНГ ; 76019, м . Івано - Франківськ , вул . Карпатська , 15; тел . (03422) 46067
  • В. Р. Процюк ІФНТУНГ ; 76019, м . Івано - Франківськ , вул . Карпатська , 15; тел . (03422) 46067
  • Г. Я. Процюк ІФНТУНГ ; 76019, м . Івано - Франківськ , вул . Карпатська , 15; тел . (03422) 46067

Keywords:

oil and gas object, intelligent systems, rules, fuzzy rules, knowledge, logical inference, fuzziness, uncertainty, knowledgebase, fuzzy logic.

Abstract

The structure, methods and means for the usage of rules in intelligent information systems that can operate with oil and gas objects were analyzed; and also the usage of rules in intelligent information systems in terms of date-bases and knowledge about oil and gas objects were studied. There were pointed out the following classes and functionality of rules: definite rules, rules with uncertainty, rules with certainty factors, fuzzy rules with linguistic entries. We also studied the structure of formal approach of intelligent control implementation in automated systems on the basis of fuzzy logic and fuzzy sets with central element in the form of fuzzy rules knowledgebase. There was proposed a structuring for functionality of cycle stages in fuzzification and defuzzification of input set of crispy data about generation of control action on the set of oil and gas objects and their components; we defined the main procedural elements for organization of fuzzy inference output, functionality peculiarities of compositional transformations and the entity of defuzzification procedure of the results in data intelligent processing within the automated intellectual system with an objective to transmit them to the lower control level in the crispy form. The main result of the research is defining of conception for construction of effective control in automated intelligent systems by means of classifying data working set on the following categories: structured, unstructured, semistructured, crispy and fuzzy data, and defining of the strategy for intelligent control based on the rules with linguistic entries and application of methods for verification of membership functions.

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References

1 Заде Л. Понятие лингвистической переменной и его применение к принятию приближенных решений / Л. Заде. – М.: Мир, 1976. – 176 с.
2 Zadeh L. A. Fuzzy sets // Information and Control. – 1965. – 8 (3). – Р. 338–353.
3 Юрчишин В. М. Інформаційне моделювання нафтогазових об’єктів: монографія / В. М. Юрчишин, В. І. Шекета, О. В. Юрчишин – Івано-Франківськ: Вид-во Івано-Франківського нац. техн. ун-ту нафти і газу, 2010 – 196 с.
4 Круглов В. В. Интеллектуальные информационные системы: компьютерная поддержка систем нечеткой логики и нечеткого вывода / В. В. Круглов, М. И. Дли. – М.: Физматлит, 2002. – 252 с.
5 Круглов В. В. Нечёткая логика и искусственные нейронные сети / В. В. Круглов, М. И. Дли, Р. Ю. Голунов. – М.: Физматлит, 2001. – 221с.
6 Демчина М. М. Формальні методи інтерпретації даних та знань про нафтогазові об’єкти / М. М. Демчина, В. Р. Процюк, В. І. Шекета // Науковий вісник Івано-Франківського національного технічного університету нафти і газу. – 2011. – №1. – С. 100-108.
7 Демчина М. М. Моделювання нафтогазової предметної області на основі фреймовопродукційного підходу / М. М. Демчина, В. Р. Процюк, В. І. Шекета // Збірник наукових праць національного гірничого університету. – Дніпропетровськ, 2011. – №36. – Т. 1. – С. 98- 105.
8 Dubois D. Fuzzy rules in knowledgebased systems – Modelling gradedness, uncertainty and preference./ D. Dubois, H. Prade // An introduction to Fuzzy Logic Applications in Intelligent Systems. – 1992. – Vol. 165. – P. 45-68.
9 Dubois D. Fuzzy sets in approximate reasoning. Part 1: Inference with possibility distributions / D. Dubois, H. Prade // Fuzzy Sets and Systems – Special memorial volume on foundations of fuzzy reasoning. – 1991. – 5 March. – Vol. 40. Iss. 1. – P. 143-202.
10 Dubois D. Gradual inference rules in approximate reasoning / D. Dubois, H. Prade // Information Sciences. – 1992. – 1-2 May. – Vol. 61 Iss. – P. 103-122.

Published

15.11.2011

How to Cite

Демчина, М. М., Процюк, В. Р., & Процюк, Г. Я. (2011). THE IMPLEMENTATION OF FORMAL METHODS FOR INTELLIGENT CONTROL BASED ON FUZZY KNOWLEDGE ABOUT THE OIL AND GAS OBJECTS. Oil and Gas Power Engineering, (3(16), 96–107. Retrieved from https://nge.nung.edu.ua/index.php/nge/article/view/77

Issue

Section

SCIENCE AND MODERN TECHNOLOGIES

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