Static and dynamic modeling of solid oxide fuel cell using genetic programming

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@Article{Chakraborty2009740,
  author =       "Uday Kumar Chakraborty",
  title =        "Static and dynamic modeling of solid oxide fuel cell
                 using genetic programming",
  journal =      "Energy",
  volume =       "34",
  number =       "6",
  pages =        "740--751",
  year =         "2009",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2009.02.012",
  URL =          "http://www.sciencedirect.com/science/article/B6V2S-4W32975-1/2/c334dcacd8fee2c381ecd788e82d33fc",
  keywords =     "genetic algorithms, genetic programming, Solid oxide
                 fuel cell, SOFC stack, Dynamic model, Transient
                 response, Neural network",
  abstract =     "Modeling of solid oxide fuel cell (SOFC) 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.
                 Several algorithmic approaches have in the past been
                 reported for the modeling of solid oxide fuel cell
                 stacks. However, all of these models have their
                 limitations. This paper presents an efficient genetic
                 programming approach to SOFC modeling and simulation.
                 This method, belonging to the computational
                 intelligence paradigm, is shown to outperform the
                 state-of-the-art radial basis function neural network
                 approach for SOFC modeling. Both static (fixed load)
                 and dynamic (load transient) analyses are provided.
                 Statistical tests of significance are used to validate
                 the improvement in solution quality.",
}

Genetic Programming entries for Uday K Chakraborty

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