Abo Bibliothek: Guest
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen
Telecommunications and Radio Engineering
SJR: 0.203 SNIP: 0.44 CiteScore™: 1

ISSN Druckformat: 0040-2508
ISSN Online: 1943-6009

Volumes:
Volumen 79, 2020 Volumen 78, 2019 Volumen 77, 2018 Volumen 76, 2017 Volumen 75, 2016 Volumen 74, 2015 Volumen 73, 2014 Volumen 72, 2013 Volumen 71, 2012 Volumen 70, 2011 Volumen 69, 2010 Volumen 68, 2009 Volumen 67, 2008 Volumen 66, 2007 Volumen 65, 2006 Volumen 64, 2005 Volumen 63, 2005 Volumen 62, 2004 Volumen 61, 2004 Volumen 60, 2003 Volumen 59, 2003 Volumen 58, 2002 Volumen 57, 2002 Volumen 56, 2001 Volumen 55, 2001 Volumen 54, 2000 Volumen 53, 1999 Volumen 52, 1998 Volumen 51, 1997

Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i14.80
pages 1303-1315

INTELLIGENT CLASSIFICATION OF BIOPHYSICAL SYSTEM STATES USING FUZZY INTERVAL LOGIC

I. S. Tvoroshenko
Kharkiv National University of Radio Electronics, 14 Nauka Ave, Kharkiv 61166, Ukraine
V. О. Gorokhovatskyi
Kharkiv National University of Radio Electronics, 14 Nauka Ave., Kharkiv, 61166, Ukraine

ABSTRAKT

The task of increasing the reliability of the adoption of classification decisions on the state of the biophysical system with fuzzy interval representations about the characteristics or properties of objects is solved. A formalized model for classifying the states of the research object is proposed. The model provides a mechanism for calculating the confidence coefficient for each situation from a defined set of space states of the system and provides an opportunity to present the investigated features of objects based on four types of membership functions, thus ensuring that the inaccuracies, fuzziness or unreliability of the available data and knowledge are eliminated. The proposed modification of the state classification method generates and evaluates several alternatives according to criteria when making classification decisions. Experimental testing of the system has been performed through software simulation, as well as costs, required for the software product development, have been calculated. The increase of the state classification reliability has been confirmed, the versatility of the proposed intellectual methods for the arbitrary set of fuzzy data has been established.

REFERENZEN

  1. Yang, B. and Li, H., (2o18) A novel dynamic timed fuzzy Petri nets modeling method with applications to industrial processes, Expert Syst. Appl., 97, pp. 276-289.

  2. Kucherenko, Ye.I., Filatov, V.A., Tvoroshenko, I.S., and Baidan, R.N., (2oo5) Intellectual Technologies in Decision-Making Technological Complexes Based on Fuzzy Interval Logic, East European Journal of Advanced Technologies, 2, pp. 92-96, (in Russian).

  3. Gorokhovatsky, V.A. and Zamula, A.A., (2o16) Employment of Intelligent Technologies in Multiparametric Control Systems, Telecommunications and Radio Engineering, 75(17), pp. 1775-1785.

  4. Avrunin, O.G., Bodiansky, Ye.V., Kalashnik, M.V., Semenets, V.V., and Filatov, V.O., (2o18) Modern Intelligent Technologies of Functional Medical Diagnostics, Kharkiv, Ukraine: KNURE, pp. 37-55, (in Ukrainian).

  5. Zhang, J.H., Xia, J.J., Garibaldi, J.M., Groumpos, P.P., and Wang, R.B., (2o17) Modeling and control of operator functional state in a unified framework of fuzzy inference Petri nets, Comput. Methods Prog. Biomed., 144, pp. 147-163.

  6. Cox, A. and Gifford, F., (1997) An overview of geographic information systems, Journal of Academic Librarianship, 23, pp. 449-461.

  7. Egorov, A.S. and Shaykin, A.N., (2oo2) Logical modeling under uncertainty based on fuzzy interval Petri nets, News of the Russian Academy of Sciences, Theory and Control Systems, 2, pp. 134-139, (in Russian).

  8. Kucherenko, Ye.I. and Tvoroshenko, I.S., (2o11) Operative evaluation of the space of states of complex distributed objects using fuzzy interval logic, Artificial Intelligence, 3, pp. 382-387, (in Ukrainian).

  9. Tvoroshenko, I.S., (2oo4) Structure and functions of intelligent decision-making tools in complex systems, Artificial Intelligence, 4, pp. 462-470, (in Russian).

  10. Kuzmin, E.A., (2o14) Logic of Interval Uncertainty, Modern Applied Science, 8, pp. 152-168.

  11. Tvoroshenko, I.S., (2o1o) Analysis of Decision-Making Processes in Intelligent Systems, Information Processing Systems, 2, pp. 248-253, (in Russian).

  12. Srinath, K.R., (2o17) Python - The Fastest Growing Programming Language, International Research Journal of Engineering and Technology, 4, pp. 354-357.

  13. Rashid, B. and Rehmani, M.H., (2o16) Applications of Wireless Sensor Networks for Urban Areas: A Survey, Journal of Network and Computer Applications, 60, pp. 192-219.

  14. Liu, H.C., Luan, X., Li, Z., and Wu, J., (2o18) Linguistic Petri Nets Based on Cloud Model Theory for Knowledge Representation and Reasoning, IEEE Trans. Knowl. Data Eng., 30, pp. 717-728.


Articles with similar content:

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION
Telecommunications and Radio Engineering, Vol.79, 2020, issue 9
I. S. Tvoroshenko, N. V. Vlasenko, V. О. Gorokhovatskyi
The System for Speech Recognition on the Basis of the Neural Network
Telecommunications and Radio Engineering, Vol.62, 2004, issue 1-6
V. A. Pimenov
MODIFICATION OF THE BRANCH AND BOUND METHOD TO DETERMINE THE EXTREMES OF MEMBERSHIP FUNCTIONS IN FUZZY INTELLIGENT SYSTEMS
Telecommunications and Radio Engineering, Vol.78, 2019, issue 20
I. S. Tvoroshenko, V. О. Gorokhovatskyi
EMPLOYMENT OF INTELLIGENT TECHNOLOGIES IN MULTIPARAMETRIC CONTROL SYSTEMS
Telecommunications and Radio Engineering, Vol.75, 2016, issue 19
V. A. Gorokhovatskiy, А. А. Zamula
COMPRESSION OF DESCRIPTIONS IN THE STRUCTURAL IMAGE RECOGNITION
Telecommunications and Radio Engineering, Vol.70, 2011, issue 15
V. A. Gorokhovatskiy