Genetic programming model of solid oxide fuel cell stack: first results

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  author =       "Uday K. Chakraborty",
  title =        "Genetic programming model of solid oxide fuel cell
                 stack: first results",
  journal =      "International Journal of Information and Communication
                 Technology (IJICT)",
  year =         "2008",
  volume =       "1",
  number =       "3/4",
  pages =        "453--461",
  keywords =     "genetic algorithms, genetic programming, solid oxide
                 fuel cells, SOFC stack, modelling, nonlinear dynamics,
  publisher =    "Inderscience Publishers",
  ISSN =         "1741-8070",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  URL =          "",
  DOI =          "doi:10.1504/IJICT.2008.024015",
  abstract =     "Models that predict performance are important tools in
                 understanding and designing solid oxide fuel cells
                 (SOFCs). Modelling of 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.
                 Several algorithmic approaches have already been
                 reported for the modelling of solid oxide fuel cell
                 stack-based systems. This paper presents a new, genetic
                 programming approach to SOFC modelling. Initial
                 simulation results obtained with the proposed approach
                 outperform the state-of-the-art radial basis function
                 neural network method for this task.",

Genetic Programming entries for Uday K Chakraborty