Semi-quantitative System Identiication

System identiication takes a space of possible models and a stream of observational data of a physical system, and attempts to identify the element of the model space that best describes the observed system. In traditional approaches, the model space is speciied by a parameterized diierential equation, and identiication selects numerical parameter values so that simulation of the model best matches the observations. We present SQUID, a method for system identiication in which the space of potential models is deened by a semi-quantitative diierential equation (SQDE): qualitative and monotonic function constraints as well as bounding intervals and functional envelopes bound the set of possible models. SQSIM predicts semi-quantitative behavior descriptions from the SQDE. Identiication takes place by describing the observation stream in similar semi-quantitative terms and intersecting the two descriptions to derive narrower bounds on the model space. Reenement is done by refuting impossible or implausible subsets of the model space. SQUID therefore has strengths, particularly robustness and expressive power for incomplete knowledge, that complement the properties of traditional system identiication methods. We also present detailed examples, evaluation, and analysis of SQUID. See note at end of paper.