A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection
a case study of rail defects
Wassamon Phusakulkajorn (TU Delft - Railway Engineering)
Jurjen Hendriks (TU Delft - Railway Engineering)
Jan Moraal (TU Delft - Railway Engineering)
R.P.B.J. Dollevoet (TU Delft - Railway Engineering)
Zili Li (TU Delft - Railway Engineering)
Alfredo Núñez (TU Delft - Railway Engineering)
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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.