Advertisement

Hybrid Fuzzy Neural Model Based Dempster-Shafer System for Processing of Diagnostic Information

  • Alexander I. Dolgiy
  • Sergey M. Kovalev
  • Andrey V. SukhanovEmail author
  • Vitezslav Styskala
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

The paper presents a technique of data fusion obtained from the various sensors. The technique is proposed as important in reliable control over state of technical objects. It is based on adaptive models, which are combinations of fuzzy neural systems and network models implementing Dempster-Shafer computational methodology of evidence combining. The structure of fuzzy neural network is presented to compute basic belief assignments. The structure of hybrid network model based on synthesis of the fuzzy neural system and adaptive net of evidence combining is proposed. It is shown that presented idea provides probabilistic inference based on multi-sensor data even when belief assignments are missed. This advantage is achieved due to experimental based training. As well, linear convergence is proved for training.

Keywords

Dempster-Shafer theory Fuzzy neural inference Technical objects control Hybrid classification models 

Notes

Acknowledgement

The work was supported Grant No. SP2018/163 “Diagnostics, reliability and efficiency of electrical machines and devices, problems of antenna systems” and by RFBR (Grants No. 17-20-01040 ofi_m_RZD, No. 16-07-00032-a and No. 16-07-00086-a).

References

  1. 1.
    Liggins II, M., Hall, D., Llinas, J.: Handbook of Multisensor Data Fusion: Theory and Practice. CRC press (2017)Google Scholar
  2. 2.
    Bleiholder, J., Naumann, F.: Data fusion and conflict resolution in integrated information systems. Ph.D. thesis, University of Potsdam (2010)Google Scholar
  3. 3.
    Valet, L., Mauris, G., Bolon, P.: A statistical overview of recent literature in information fusion. IEEE Aerosp. Electron. Syst. Mag. 16(3), 7–14 (2001)CrossRefGoogle Scholar
  4. 4.
    Goodman, I.R., Mahler, R.P., Nguyen, H.T.: Mathematics of Data Fusion, vol. 37. Springer Science & Business Media (2013)Google Scholar
  5. 5.
    Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton university press (1976)Google Scholar
  6. 6.
    Kovalev, S.M., Tarassov, V.B., Dolgiy, A.I., Dolgiy, I.D., Koroleva, M.N., Khatlamadzhiyan, A.E.: Towards intelligent measurement in railcar on-line monitoring: from measurement ontologies to hybrid information granulation system. In: International Conference on Intelligent Information Technologies for Industry, pp. 169–181. Springer (2017)Google Scholar
  7. 7.
    Batyrshin, I., Kaynak, O.: Parametric classes of generalized conjunction and disjunction operations for fuzzy modeling. IEEE Trans. Fuzzy Syst. 7(5), 586–596 (1999)CrossRefGoogle Scholar
  8. 8.
    Chai, Y., Jia, L., Zhang, Z.: Mamdani model based adaptive neural fuzzy inference system and its application. Int. J. Comput. Intell. 5(1), 22–29 (2009)Google Scholar
  9. 9.
    Denoeux, T.: A neural network classifier based on dempster-shafer theory. IEEE Trans. Syst. Man Cybern. -Part A: Syst. Hum. 30(2), 131–150 (2000)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Bishop, C., Bishop, C.M., et al.: Neural Networks for Pattern Recognition. Oxford university press (1995)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alexander I. Dolgiy
    • 1
  • Sergey M. Kovalev
    • 1
  • Andrey V. Sukhanov
    • 1
    • 2
    Email author
  • Vitezslav Styskala
    • 2
  1. 1.JSC “NIIAS”, Rostov BranchRostov-on-DonRussia
  2. 2.VSB - Technical University of OstravaOstravaCzech Republic

Personalised recommendations