TY - GEN
T1 - A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection
T2 - 2022 IEEE International Conference on Fuzzy Systems
AU - Phusakulkajorn, W.
AU - Hendriks, J.M.
AU - Moraal, J.
AU - Dollevoet, R.P.B.J.
AU - Li, Z.
AU - Nunez, Alfredo
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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. Thenumerical 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.
AB - 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. Thenumerical 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.
KW - spiking neural network
KW - parameter uncertainty
KW - prediction interval
KW - interpretability
KW - anomaly detection
UR - http://www.scopus.com/inward/record.url?scp=85138816090&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE55066.2022.9882864
DO - 10.1109/FUZZ-IEEE55066.2022.9882864
M3 - Conference contribution
VL - 2022
BT - 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PB - IEEE
Y2 - 18 July 2022 through 23 July 2022
ER -