Fuzzy modeling of measurement data acquired from physical sensors (original) (raw)

The measurement uncertainty in physical sensors is often represented by a probabilistic approach, but such a representation is not always adapted to new intelligent systems. Therefore, a fuzzy representation, based on the possibility theory, can sometimes be preferred. We previously proposed a truncated triangular probability-possibility transformation to be applied to any unimodal and symmetric probability distribution which can be assimilated to one of the four most encountered probability laws (Gaussian, double-exponential, triangular, uniform). In this paper, we propose to build a fuzzy model of data acquired from physical sensors by applying this transformation. For this purpose, a minimum of knowledge about the probabilistic modeling of sensors is required. Three main situations will be considered and for each situation, an adapted fuzzy modeling will be proposed.