Recognizing signal trends on-line by a fuzzy-logic-based methodology optimized via genetic algorithms

The present work addresses the problem of on-line signal trend identification within a fuzzy logic-based methodology previously proposed in the literature. A modification in the application of the methodology is investigated which entails the use of singletons instead of triangular fuzzy numbers for the characterization of the truth values of the six parameters describing the dynamic trend of the evolving process. Further, calibration of the model is performed by a genetic algorithm procedure. In an example of application of the method, this procedure is also exploited for feature selection, i.e. for choosing which of the measured plant signals are relevant for the transient identification.