An Evolutionary Computation Approach to Predicting Output Voltage from Fuel Utilization in SOFC Stacks

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@InProceedings{Chakraborty2:2009:cec,
  author =       "Uday K. Chakraborty",
  title =        "An Evolutionary Computation Approach to Predicting
                 Output Voltage from Fuel Utilization in SOFC Stacks",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2165--2171",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P686.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983209",
  size =         "7 pages",
  abstract =     "Modeling of solid oxide fuel cell (SOFC) stack based
                 systems is a powerful approach that can provide useful
                 insights into the nonlinear dynamics of the system
                 without the need for formulating complicated systems of
                 equations describing the electrochemical and thermal
                 properties. This paper presents an efficient genetic
                 programming approach for modeling and simulation of
                 SOFC output voltage versus fuel burn behavior. This
                 method is shown to outperform the state-of-the-art
                 radial basis function neural network approach for SOFC
                 modeling.",
  keywords =     "genetic algorithms, genetic programming, RBFANN",
  notes =        "Fuel cell hydrogen + oxygen = steam + 1.18volts at
                 1000Centigrade and 1bar. DSS \cite{ga94aGathercole}
                 Discipulus. NeuroSolutions.

                 CEC 2009 - A joint meeting of the IEEE, the EPS and the
                 IET. IEEE Catalog Number: CFP09ICE-CDR",
}

Genetic Programming entries for Uday K Chakraborty

Citations