Controlling with words using automatically identified fuzzy Cartesian granule feature models

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Baldwin, J., F., Martin, T., P. and Shanahan, J., G.

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Info: International Journal of Approximate Reasoning (Journal), 1999, p. 109-148
Keywords:genetic algorithms, genetic programming
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 [SI] 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 [GP] with a novel and cheap fitness function, [FF] which relies on the semantic separation of concepts expressed in terms of Cartesian granule fuzzy sets, [FS] 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.
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BibTex:
@Article{Baldwin:1999:IJAR,
  author =       "James F. Baldwin and Trevor P. Martin and James G.
                 Shanahan",
  title =        "Controlling with words using automatically identified
                 fuzzy Cartesian granule feature models",
  journal =      "International Journal of Approximate Reasoning",
  volume =       "22",
  pages =        "109--148",
  year =         "1999",
  number =       "1-2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e",
  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.",
}