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)


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.


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



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).


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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

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