Semi-Quantitative System Identi cation
نویسندگان
چکیده
System identi cation 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 speci ed by a parameterized di erential equation, and identi cation selects numerical parameter values so that simulation of the model best matches the observations. We present SQUID, a method for system identi cation in which the space of potential models is de ned by a semi-quantitative di erential 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. Identi cation 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. Re nement 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 identi cation methods. We also present detailed examples, evaluation, and analysis of SQUID. keywords: qualitative reasoning; system identi cation; qualitative simulation; monitoring; diagnosis; imprecise models See note at end of paper.
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تاریخ انتشار 2010