Fuzzy symbolic sensors—From concept to applications

Abstract This paper deals with sensors which compute and report liguistic assessments of numerical acquired values. Such sensors, called symbolic sensors, are particularly adapted when working with control systems which use artificial intelligence techniques. After having reconsidered some elements of the measurement theory, this paper sets the foundations of the symbolic sensors by introducing the meaning of a lexical value and the description of a numeric measurement as two mappings that link the set of the subsets of the numerical domain with the set of the subsets of the lexical domain. It is then shown how the fuzzy subset theory provides a smart way for the treatment of symbol graduality, for measurement imprecisions and measurement validation, and for taking into account the measurement context. This approach leads to introducing a specific structure into the sensors, then called fuzzy symbolic sensors. As application, two specimens of fuzzy symbolic sensors have been successfully implemented. The first is an ultrasonic range finding sensor, which uses a procedure of management of errors and a procedure of creation of concepts by semantic relationships. The second is a colour matching sensor, which uses an interpolation method for creating the concepts by learning with a teacher.