Induction of Virtual Sensors with Function Stacks

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Daniel Ashlock and Adam J. Shuttleworth and 
                 Kenneth M. Bryden",
  title =        "Induction of Virtual Sensors with Function Stacks",
  booktitle =    "ANNIE 2009, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2009",
  editor =       "Cihan H. Dagli and K. Mark Bryden and 
                 Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer",
  volume =       "19",
  address =      "St. Louis, MO, USA",
  note =         "Part I",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9780791802953",
  DOI =          "doi:10.1115/1.802953.paper4",
  abstract =     "Virtual sensors are mathematical models that predict
                 the readings of a sensor in a location currently
                 without an operational sensor. Virtual sensors can be
                 used to compensate for a failed sensor or as a
                 framework for supporting mathematical decomposition of
                 a model of a complex system. This study applies a novel
                 genetic programming representation called a function
                 stack to the problem of virtual sensor induction in a
                 simple thermal system. Real-valued function stacks are
                 introduced in this study. The thermal system modelled
                 is a heat exchanger. Function stacks are found to be
                 able to efficiently find compact and accurate models
                 for each often sensors using the data from the other
                 sensors. This study serves as proof-of-concept for
                 using function stacks as a modeling technology for
                 virtual sensors.",

Genetic Programming entries for Daniel Ashlock Adam J Shuttleworth Kenneth M Bryden