A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects

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Abstract

In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy intervals. The multiple spiking neural networks are used to capture different sources of uncertainties. In this paper, we consider uncertainties in the parameters of spiking neural networks during the training phase. The proposed method comprises two steps. In the first step,
multiple sets of the firing times for both classes are obtained from multiple spiking neural networks. In the second step, the obtained multiple sets of firing times are fuzzy numbers and they are used to construct fuzzy intervals. The proposed method is showcased with the problem of rail defect detection. The
numerical analysis indicates that the fuzzy intervals are suitable to make use of the information provided by the multiple spike neural networks. Finally, with the proposed method, we improve the interpretability of the decision making regarding the detection of anomalies.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
Volume2022
ISBN (Electronic)978-1-6654-6710-0
Publication statusPublished - 2022
Event2022 IEEE International Conference on Fuzzy Systems - Centro Congressi Padova, Padua, Italy
Duration: 18 Jul 202223 Jul 2022
https://wcci2022.org/

Conference

Conference2022 IEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • spiking neural network
  • parameter uncertainty
  • prediction interval
  • interpretability
  • anomaly detection

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