IDENTIFICATION WITH FINITELY MANY DATA POINTS: THE LSCR APPROACH

This paper gives an overview of LSCR (Leave-out Sign-dominant Correlation Regions), a general technique for system identification. Under normal conditions, observations contain information corrupted by disturbances and measurement noise so that only an approximate description of the underlying system can at best be obtained from a finite data set. This is similar to describing an object seen through a frosted glass. Differently from standard identification methods that deliver single models, LSCR generates a model set. As information increases, the model set shrinks around the true system and, for any finite sample size, the set is guaranteed to contain the true system with a precise probability chosen by the user. LSCR only assumes a minimum amount of prior information on the noise.