An adaptive function identification system

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@InProceedings{Jiang:1993:afis,
  author =       "Mingda Jiang and Alden H. Wright",
  title =        "An adaptive function identification system",
  booktitle =    "Proceedings of the IEEE/ACM Conference on Developing
                 and Managing Intelligent System Projects, Vienna,
                 Virginia, USA",
  year =         "1993",
  pages =        "47--53",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming,
                 Levenberg-Marquardt nonlinear regression algorithm,
                 adaptive function identification system, adaptive
                 system, expression-tree representation, symbolic
                 function identification problem, adaptive systems,
                 learning (artificial intelligence)",
  DOI =          "doi:10.1109/DMISP.1993.248637",
  size =         "7 pages",
  abstract =     "Given data in the form of a collection of (x,y) pairs
                 of real numbers, the symbolic function identification
                 problem is to find a functional model of the form
                 y=f(x) that fits the data. This paper describes an
                 adaptive system for solution of symbolic function
                 identification problems that combines a genetic
                 algorithm and the Levenberg-Marquardt nonlinear
                 regression algorithm. The genetic algorithm uses an
                 expression-tree representation rather than the more
                 usual binary-string representation. Experiments were
                 run with data generated using a wide variety of
                 function models. The system was able to find a function
                 model that closely approximated the data with a very
                 high success rate",
  notes =        "HGSFI, Ultrix, Unidata Inc. Also known as
                 \cite{248637}",
}

Genetic Programming entries for Mingda Jiang Alden H Wright

Citations