Semi-Quantitative System Identification

System identification 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 specified by a parameterized differential equation, and identification selects numerical parameter values so that simulation of the model best matches the observations. We present SQUID, a method for system identification in which the space of potential models is defined by a semi-quantitative differential equation (SQDE): qualitative and monotonic function constraints as well as numerical intervals and functional envelopes bound the set of possible models. The simulator SQSIM predicts semi-quantitative behavior descriptions from the SQDE. Identification 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. Refinement is do..