Cyber-Physical Systems (CPSs) account for technical devices with both sensing and reasoning abilities. Our interest here is in classifying time series of sensory data by fuzzy logic in CPS applications. More specifically, a time series is represented by the distribution of its data samples using a novel Intervals’ Number (IN) induction technique. We demonstrate the effectiveness of our techniques in a benchmark classifica- tion problem regarding ElectroEncephaloGraphy (EEG) signals. Mathematically sound extensions of the c-means as well as the kNN algorithms result in good classification performance com- paratively to alternative methods from the literature. Potential future enhancements of the proposed techniques are discussed.


Kaburlasos, V.G., Vrochidou, E., Panagiotopoulos, F., Aitsidis, C., Jaki, A.: Time series classification in cyber-physical system applications by intervals’ numbers techniques. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019). pp. 125–130, New Orleans, Louisiana, USA (2019).

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