Controlling with words using automatically identified fuzzy Cartesian granule feature models
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- @Article{Baldwin:1999:IJAR,
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author = "James F. Baldwin and Trevor P. Martin and
James G. Shanahan",
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title = "Controlling with words using automatically identified
fuzzy Cartesian granule feature models",
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journal = "International Journal of Approximate Reasoning",
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volume = "22",
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pages = "109--148",
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year = "1999",
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number = "1-2",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e",
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abstract = "We present a new approach to representing and
acquiring controllers based upon Cartesian granule
features - multidimensional features formed over the
cross product of words drawn from the linguistic
partitions of the constituent input features -
incorporated into additive models. Controllers
expressed in terms of Cartesian granule features enable
the paradigm {"}controlling with words{"} by
translating process data into words that are
subsequently used to interrogate a rule base, which
ultimately results in a control action. The system
identification of good, parsimonious additive Cartesian
granule feature models is an exponential search
problem. In this paper we present the G_DACG
constructive induction algorithm as a means of
automatically identifying additive Cartesian granule
feature models from example data. G_DACG combines the
powerful optimisation capabilities of genetic
programming with a novel and cheap fitness function,
which relies on the semantic separation of concepts
expressed in terms of Cartesian granule fuzzy sets, in
identifying these additive models. We illustrate the
approach on a variety of problems including the
modelling of a dynamical process and a chemical plant
controller.",
- }
Genetic Programming entries for
James F Baldwin
Trevor P Martin
James G Shanahan
Citations