Assessment the Quality of Telecommunication Services by Fuzzy Inferences System
Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
[1] International Telecommunication Union Recommendation.
http://www.itu.int/rec/recommendation.asptype=series&lang=e&parent=
T-REC (2012). Accessed 1 July 2014.
[2] J. Kacprzyk, R.R. Yager, Management Decision Support Systems Using
Fuzzy Sets and Possibility Theory. Cologne: Verlag TUV Rheinland,
1985.
[3] R.R. Rzaev, Intellektualniy Analiz Dannykh v Sistemakh Podderzhki
Priniatiia Reshenii (Data Mining in Decision Support Systems).
Moscow: Lambert Academic Publishing, 2013 (in Russian).
[4] R.R. Rzaev, A.I. Goyushov, “Mnogokriterialnaia otsenka kachestva
telekommunikatsionnykh uslug na osnove primeneniia mekhanizma
nechetkogo vyvoda” (“Multi-criteria estimation of the quality of
communication services on the base of using of fuzzy inference
mechanism”), AMEA Xabarlary, vol. 33, no. 6, 2014, pp. 56–66 (in
Russian).
[5] D. Sokolov, “Nechetkaia sistema otsenki kachestva” (“Fuzzy system of
quality estimation”), Technology and communication facilities, no. 4,
2009, pp. 26–28 (in Russian).
[6] R.R. Yager, L.A. Zadeh, An Introduction to Fuzzy Logic Applications in
Intelligent Systems. Norwell: Kluwer Academic Publisher, 1992
[7] R.R. Yager, L.A. Zadeh, Fuzzy Sets, Neural Networks and Soft
Computing. New York: Van Nostrand Reinhold, 1994.
[8] L.A. Zadeh, “Rol miagkikh vychislenii i nechetkoi logiki v ponimanii,
konstruirovanii i razvitii informatsionnykh/intellektualnykh sistem”
(“The role of Soft Computing and Fuzzy Logic in understanding, design
and development of information/intelligent systems”), Novosti
iskusstvennogo intellekta, no. 2-3, 2001, pp. 7–11.
[9] L.A. Zadeh, The Concept of a Linguistic Variable and its Application to
Approximate Reasoning. New York: American Elsevier Publishing
Company, 1974.
[10] H. Zimmerman, Fuzzy Sets Theory and Its Applications. New York:
Kluwer Academic Publishers, 2001.
[11] J.A. Bernard, “Use of rule-based system for process control”, Control
System Magazine, vol. 8, no. 5, 1988, pp. 3–13.
[12] M.Braae, D.A. Rutherford, “Selection of parameters for a fuzzy logic
controller”, Fuzzy Sets Systems, vol. 2, no. 3, 1979, pp. 185–199.
[13] B. Kosko, Neural Networks and Fuzzy Systems. New York: Englewood
Cliffs, 1992.
[14] C.T. Lin, C.S. George Lee, “Neural network-based fuzzy logic control
and decision system”, Transactions on Computers, vol. 40, no. 12, 1991,
pp. 1320–1336.
[15] C.T. Lin, C.S. George Lee, “Supervised and unsupervised learning with
fuzzy similarity for neural network-based fuzzy logic control systems”.
In: Yager, R.R., Zadeh, L.A. (ed) Fuzzy Sets, Neural Networks, and Soft
Computing, New York: Van Nostrand Reinhold, 1994, pp. 85–125.
[16] T.J. Procyk, E.H. Mamdani, “A linguistic self-organizing process
controller”, Automatica, vol. 15, no. 1, 1979, pp. 15–30.
[17] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning internal
representations by error propagation”, Parallel Distributed Processing,
no. 1, 1986, pp. 318–362.
[18] M. Sugeno (ed.), Industrial Applications of Fuzzy Control. Amsterdam:
Elsevier Science Publishers, 1985, pp. 231–239.
[19] T. Takagi, M. Sygeno, “Derivation of fuzzy control rules from human
operator’s actions”, in Proc. IFAC Symposium. Fuzzy Information
Knowledge Representation Decision Analysis, Marseilles, 1983, pp. 55–
60.
[20] R.M. Tong, “Synthesis of fuzzy models for industrial processes”, Int.
Gen Systems, no. 4, 1978, pp. 143–162.
[1] International Telecommunication Union Recommendation.
http://www.itu.int/rec/recommendation.asptype=series&lang=e&parent=
T-REC (2012). Accessed 1 July 2014.
[2] J. Kacprzyk, R.R. Yager, Management Decision Support Systems Using
Fuzzy Sets and Possibility Theory. Cologne: Verlag TUV Rheinland,
1985.
[3] R.R. Rzaev, Intellektualniy Analiz Dannykh v Sistemakh Podderzhki
Priniatiia Reshenii (Data Mining in Decision Support Systems).
Moscow: Lambert Academic Publishing, 2013 (in Russian).
[4] R.R. Rzaev, A.I. Goyushov, “Mnogokriterialnaia otsenka kachestva
telekommunikatsionnykh uslug na osnove primeneniia mekhanizma
nechetkogo vyvoda” (“Multi-criteria estimation of the quality of
communication services on the base of using of fuzzy inference
mechanism”), AMEA Xabarlary, vol. 33, no. 6, 2014, pp. 56–66 (in
Russian).
[5] D. Sokolov, “Nechetkaia sistema otsenki kachestva” (“Fuzzy system of
quality estimation”), Technology and communication facilities, no. 4,
2009, pp. 26–28 (in Russian).
[6] R.R. Yager, L.A. Zadeh, An Introduction to Fuzzy Logic Applications in
Intelligent Systems. Norwell: Kluwer Academic Publisher, 1992
[7] R.R. Yager, L.A. Zadeh, Fuzzy Sets, Neural Networks and Soft
Computing. New York: Van Nostrand Reinhold, 1994.
[8] L.A. Zadeh, “Rol miagkikh vychislenii i nechetkoi logiki v ponimanii,
konstruirovanii i razvitii informatsionnykh/intellektualnykh sistem”
(“The role of Soft Computing and Fuzzy Logic in understanding, design
and development of information/intelligent systems”), Novosti
iskusstvennogo intellekta, no. 2-3, 2001, pp. 7–11.
[9] L.A. Zadeh, The Concept of a Linguistic Variable and its Application to
Approximate Reasoning. New York: American Elsevier Publishing
Company, 1974.
[10] H. Zimmerman, Fuzzy Sets Theory and Its Applications. New York:
Kluwer Academic Publishers, 2001.
[11] J.A. Bernard, “Use of rule-based system for process control”, Control
System Magazine, vol. 8, no. 5, 1988, pp. 3–13.
[12] M.Braae, D.A. Rutherford, “Selection of parameters for a fuzzy logic
controller”, Fuzzy Sets Systems, vol. 2, no. 3, 1979, pp. 185–199.
[13] B. Kosko, Neural Networks and Fuzzy Systems. New York: Englewood
Cliffs, 1992.
[14] C.T. Lin, C.S. George Lee, “Neural network-based fuzzy logic control
and decision system”, Transactions on Computers, vol. 40, no. 12, 1991,
pp. 1320–1336.
[15] C.T. Lin, C.S. George Lee, “Supervised and unsupervised learning with
fuzzy similarity for neural network-based fuzzy logic control systems”.
In: Yager, R.R., Zadeh, L.A. (ed) Fuzzy Sets, Neural Networks, and Soft
Computing, New York: Van Nostrand Reinhold, 1994, pp. 85–125.
[16] T.J. Procyk, E.H. Mamdani, “A linguistic self-organizing process
controller”, Automatica, vol. 15, no. 1, 1979, pp. 15–30.
[17] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning internal
representations by error propagation”, Parallel Distributed Processing,
no. 1, 1986, pp. 318–362.
[18] M. Sugeno (ed.), Industrial Applications of Fuzzy Control. Amsterdam:
Elsevier Science Publishers, 1985, pp. 231–239.
[19] T. Takagi, M. Sygeno, “Derivation of fuzzy control rules from human
operator’s actions”, in Proc. IFAC Symposium. Fuzzy Information
Knowledge Representation Decision Analysis, Marseilles, 1983, pp. 55–
60.
[20] R.M. Tong, “Synthesis of fuzzy models for industrial processes”, Int.
Gen Systems, no. 4, 1978, pp. 143–162.
@article{"International Journal of Information, Control and Computer Sciences:68742", author = "Oktay Nusratov and Ramin Rzaev and Aydin Goyushov", title = "Assessment the Quality of Telecommunication Services by Fuzzy Inferences System", abstract = "Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
", keywords = "Quality of communication, IP-telephony, Fuzzy set,
Fuzzy implication, Neural network.", volume = "9", number = "1", pages = "108-13", }
