Modeling the Operation of an Adaptive Computing System Based on FGPN for Case Risk Management

  • Aleksey Senkov
  • Evgenii Sorokin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 658)


The article discusses problems of case risk management modeling using an example of an adaptive heterogeneous computer system. Existing approaches to complex system modeling, as a rule, do not allow modeling of systems that operate under risk conditions and are able to adapt to occurring risk events. An approach based on nested Petri nets is proposed (growing Petri nets). Growing Petri nets provide an opportunity to simulate the system operation in case of risk occurrence. An example of a growing Petri net for an adaptive computing system is given.


Growing Petri nets Heterogeneous computing system Risk management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shang, K., Kossen, Z.: Applying Fuzzy Logic to Risk Assessment and Decision-Making. Casualty Actuarial Society. Canadian Institute of Actuaries, Society of Actuaries (2013).Google Scholar
  2. 2.
    Shapiro, A.F., Koissi, M.C.: Risk Assessment Applications of Fuzzy Logic. Casualty Actuarial Society. Canadian Institute of Actuaries, Society of Actuaries (2015).Google Scholar
  3. 3.
    Sugeno, M.: Fuzzy Identification of Systems and its Applications to Modeling and Control, pp. 116-132. IEEE Transactions on Systems, Man, and Cybernetics SMC-15(1) (1985).Google Scholar
  4. 4.
    Mamdani, E.H.: Application of Fuzzy Logic to Approximate Reasoning Using Linguistic systems, pp. 1182-1191. IEEE Transactions on Computers (1978).Google Scholar
  5. 5.
    Zeidler, J., Schlosser, M., Ittner, A., Posthoff, C.: Fuzzy decision trees and numerical attributes. Fuzzy Systems, pp. 985-990. Proceedings of the Fifth IEEE International Conference on Volume: 2 (1996).Google Scholar
  6. 6.
    Fan, L.T., Lai, F.S.: Toguchi, K.: Fault-Tree Analysis by Fuzzy Probability IEEE Transactions on Reliability, pp. 453-457. Volume: R-32, Issue: 5 (1983).Google Scholar
  7. 7.
    Gmytrasiewicz, P., Hassberger, J.A., Lee, J.C.: Fault tree based diagnostics using fuzzy logic. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1115-1119 Volume: 12, Issue: 11 (1990).Google Scholar
  8. 8.
    Pan, H., Liu, L. Fuzzy Bayesian networks – a general formalism for representation, in-ference and learning with hybrid Bayesian networks – IJPRAI. V. 14(7), pp. 941–962. (2000).Google Scholar
  9. 9.
    Styblinski, M.A., Meyer, B.D.: Fuzzy cognitive maps, signal flow graphs, and qualitative circuit analysis, pp. 549 – 556. Neural Networks (1988).Google Scholar
  10. 10.
    Pedrycz, W., Gomide F.: A generalized fuzzy Petri net model, pp. 295 – 301. IEEE TransactionsonFuzzySystems, (1994).Google Scholar
  11. 11.
    Shiladitya Pujari, Sripati Mukhopadhyay,”Petri Net: A Tool for Modeling and Analyze Multi-agent Oriented Systems”, International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, pp.103-112, 2012. DOI:  10.5815/ijisa.2012.10.11
  12. 12.
    Senkov, A.: Risk Managemenet: Intelligent Models, Methods and Software (in Russian), p. 222 (2016).Google Scholar
  13. 13.
    Reza Fotohi, Mehdi Effatparvar,”A Cluster Based Job Scheduling Algorithm for Grid Computing”, International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.12, pp.70-77, 2013. DOI:  10.5815/ijitcs.2013.12.09
  14. 14.
    Lomazova, I.A.: On Proving Large Distributed Systems: Petri Net Modules Verification, pp. 70–75. Proc. 4th Int. Conference on Parallel Computing Technologies. Lecture Notes in Computer Science. Vol. 1277 (1997).Google Scholar
  15. 15.
    Lomazova, I.A. Nested Petri nets — a Formalism for Specification and Verification of Multi-Agent Distributed Systems, pp. 195–214. Fundamenta Informaticae. Vol. 43. №1–4 (2000).Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Moscow Power Engineering InstituteNational Research UniversitySmolenskRussia

Personalised recommendations