Assessment the Quality of Telecommunication Services by Fuzzy Inferences System
Authors: Oktay Nusratov, Ramin Rzaev, Aydin Goyushov
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337885
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[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.