Fuzzy modeling of measurement data acquired from physical sensors

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 are considered and for each situation, an adapted fuzzy modeling is proposed. Examples of these three situations are based on FM-chirped ultrasonic sensors.