Herbert Kay, Bernhard Rinner and Benjamin Kuipers. 2000.
Semi-quantitative system identification.
Artificial Intelligence 119: 103-140.
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 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 identification methods. We also
present detailed examples, evaluation, and analysis of SQUID.
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