Model of the Applicability of Expert System Based on Neural Networks Technology and Hybrid Systems for Decision Making

Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 317)

Abstract

In this chapter it was justified mathematical model of construction of expert systems and methods of its application in educational process. It is given the mathematical formulation of a number of tasks of application of neural networks and hybrid expert systems in the subsystem of decision-making and evaluation of knowledge. By using of fuzzy logic an original method for controlling the student’s knowledge was developed which as maximal closely simulating the behavior of the teacher in the student survey, which combines the power and laconism that was not previously available for automated systems. The proposed method of mathematical processing and designing of educational materials on the basis of linguistic variables allows the designer to simulate any configuration of educational materials is an important step in achieving individual learning.

Keywords

Hybrid expert systems Fuzzy logic Fuzzy sets Decision-making block Conceptual knowledge base Fuzzy hybrid expert systems Fuzzy neural hybrid expert systems Neural networks Fuzzy rules 

References

  1. 1.
    Abbasov, A.M.: Information boom: new trends and expectations, Springer series title: studies in fuzziness and soft computing. Soft Comput.: State Art Theory Novel Appl. 291, 1–12 (2012)Google Scholar
  2. 2.
    Kandel, A., Langholz, G.: Fuzzy Control Systems, pp. 159–187. CRC Press, Boca Raton (1993)Google Scholar
  3. 3.
    Laurene, V.F.: Fundamentals of Neural Networks: Architectures, Algorithms And Applications, pp. 103–121. Prentice Hall, Englewood Cliffs (1993)Google Scholar
  4. 4.
    Zadeh, L.A., Klir, G.J., Yuan, B.: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh, pp. 60–69 (1996)Google Scholar
  5. 5.
    Bernshteyn, L.S., Bojenyuk, A.V.: Fuzzy Models of Decision Making: Deduction, Induction, Analogy, pp. 78–99. Univ Tsure, Taganrog (2001)Google Scholar
  6. 6.
    Cordon, O., Herrera, F.: Linguistic Modeling by Hierarchical Systems of Linguistic Rules/Technical Report # DECSAI—990 114, Department of Computer Science and A. I., University of Granada, July, 1999, pp. 187–215 (1999)Google Scholar
  7. 7.
    Barsky, A.B.: Neural Networks: Recognition, Management, Decision-Making, pp. 30–63. Finance and Statistics, Moscow (2004)Google Scholar
  8. 8.
    Shahbazova, Sh., Freisleben, B.: A network-based intellectual information system for learning and testing. In: Fourth International Conference on Application of Fuzzy Systems and Soft Computing, Siegen, Germany, pp. 308–313 (2000)Google Scholar
  9. 9.
    Shahbazova, Sh.: Applied research in the field of automation of Learning and knowledge control, SPRİNGER Series Title: Studies in Fuzziness and Soft Computing. Soft Comput.: State Art Theory Novel Appl. 14, 223–240 (2012)Google Scholar
  10. 10.
    Ledeneva, T.M.: Fuzzy Information Processing: A Tutorial/TM Ledeneva, pp. 212–233. Voronezh State University, Voronezh (2006)Google Scholar
  11. 11.
    Bellman, R., Zadeh, L.A.: Decision-Making in Ambiguous Circumstances, Issues Analysis And Decision-Making, pp. 180–199. Springer, New York (1976)Google Scholar
  12. 12.
    Novak, V., Perfilieva, I., Mochkorzh, I.: Mathematical Principles of Fuzzy Logic, Trans. from English, Averkina, M. (ed.). FIZMATLIT, 209–252 (2006)Google Scholar
  13. 13.
    Borisov, VV, Kruglov, VV, Fedulov, AS.: Fuzzy Models and Networks, pp. 224–284. Hot line—Telecom, Moscow (2007)Google Scholar
  14. 14.
    Gorbunova, L.G.: On the realization of the rating system in pedagogical high schools. In: Proceedings of 2nd International Technical Conference “University Education”, Penza, 1998, Part 1, pp. 105–106 (1998)Google Scholar
  15. 15.
    Nikravesh, M., Zadeh, L.A., Kacprzyk, J.: Soft Computing for Information Processing and Analysis, pp. 93–99. Springer, New York (2005)Google Scholar
  16. 16.
    Heaton, J.: Introduction to Neural Networks for C #, 2nd edn, pp. 224–231. Heaton Research, USA (2008)Google Scholar
  17. 17.
    Hanss, M.: Applied Fuzzy Arithmetic: An Introduction with Engineering Applications, 1st edn, pp. 100–116, 139–147. Springer, Berlin (2004)Google Scholar
  18. 18.
    Zadeh, L.A.: The Concept of Linguistic Variable and Its Application to the Adoption of Approximate Solutions, pp. 140–164. Springer, New York (1976)Google Scholar
  19. 19.
    Shahbazova, Sh.: Application of fuzzy sets for control of student knowledge. Appl. Comput. Math. Int. J. 10(1), 195–208 (2008). ISSN 1683-3511 (Special Issue on Fuzzy Set Theory and Applications)Google Scholar
  20. 20.
    Zadeh, L.A., Kacprzyk, J.: Fuzzy Logic for the Management of Uncertainty, 1st edn, pp. 75–84. Wiley-Interscience, New York (1992)Google Scholar
  21. 21.
    Shahbazova, Sh.: Development of the knowledge base learning system for distance education. Int. J. Intell. Syst. 27(4), 343–354 (2012)Google Scholar
  22. 22.
    Zadeh, L.A.: The new approach to the analysis of difficulty systems and decision processes, Mathematics Today, Knowledge, pp. 23–37 (1974)Google Scholar
  23. 23.
    Shahbazova, Sh: Investigation of the basic problems and trends of traditional education systems. Int. J. Technol. Manage. Inform. Probl. Ukraine 3, 110–117 (2013)Google Scholar
  24. 24.
    Shahbazova, Sh: Simulating the behavior of the teacher, the use of expert systems in the field of educational systems, control systems and machines. J. Inst. Cybern. Glushkov Nat. Acad. Sci. Ukraine 3, 68–75 (2012)Google Scholar
  25. 25.
    Yager, R.: Fuzzy Sets and Theory of Possibilities: Recent advances, pp. 391–405. Radio and Communications, Moscow (1986)Google Scholar
  26. 26.
    Shahbazova, Sh.: Functional design of the control of knowledge on base of fuzzy logic. In: International Conference on Application of Information and Communication Technology and Statistics in Economy and Education, Sofia, Bulgaria, pp. 24–31 (2012)Google Scholar
  27. 27.
    Bouchon-Meunier, B., Yager, R.R.: Fuzzy Logic and Soft Computing (Advances in Fuzzy Systems: Application and Theory), pp. 84–93, 103–119 World Scientific, Singapore (1995)Google Scholar
  28. 28.
    Shahbazova, Sh.: Application of fuzzy sets for control of student knowledge. Appl. Comput. Math. Int. J. 10(1),195–208 (2011). ISSN 1683-3511 (Special Issue on Fuzzy Set Theory and Applications)Google Scholar
  29. 29.
    Shahbazova, Sh.: Decision-making in determining the level of knowledge of students in the learning process under uncertainty. Informatica Int. J. Comput. Inform. 37(3), 339–344 (2013). Print edition ISSN: 0350-5596Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Minister of Information and Communication Technologies of the Republic of AzerbaijanBakuAzerbaijan
  2. 2.Department of Information Technology and ProgrammingAzerbaijan Technical UniversityBakuAzerbaijan

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