Semi-quantitative system identification

System identification takes a space of possible models and a stream of obse rvational data of a physical system, and attempts to identify the element o f the model space that best describes the observed system. In traditional a pproaches, the model space is specified by a parameterized differential equ ation, and identification selects numerical parameter values so that simula tion of the model best matches the observations. We present SQUID, a method for system identification in which the space of potential models is define d by a semi-quantitative differential equation (SQDE): qualitative and mono tonic function constraints as well as numerical intervals and functional en velopes bound the set of possible models. The simulator SQSIM predicts semi -quantitative behavior descriptions from the SQDE. Identification takes pla ce by describing the observation stream in similar semi-quantitative terms and intersecting the two descriptions to derive narrower bounds on the mode l space. Refinement is done by refuting impossible or implausible subsets o f the model space. SQUID therefore has strengths, particularly robustness a nd expressive power for incomplete knowledge, that complement the propertie s of traditional system identification methods. We also present detailed ex amples, evaluation, and analysis of SQUID. (C) 2000 Elsevier Science B.V. A ll rights reserved.