Fuzzy modeling of measurement data acquired from physical sensors

The measurement uncertainty in physical sensors is often represented by a p robabilistic approach, but such a representation is not always adapted to n ew intelligent systems. Therefore, a fuzzy representation, based on the pos sibility theory, can sometimes be preferred. We previously proposed a trunc ated triangular probability-possibility transformation to be applied to any unimodal and symmetric probability distribution which can be assimilated t o one of the four most encountered probability laws (Gaussian, double-expon ential, triangular, uniform), In this paper, we propose to build a fuzzy mo del 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 ea ch situation, an adapted fuzzy modeling will be proposed. Examples of these three situations are based on FM-chirped ultrasonic sensors.