Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Garg:2016:CILS,
  author =       "Akhil Garg and B. N. Panda and D. Y. Zhao and K. Tai",
  title =        "Framework based on number of basis functions
                 complexity measure in investigation of the power
                 characteristics of direct methanol fuel cell",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "155",
  pages =        "7--18",
  year =         "2016",
  ISSN =         "0169-7439",
  DOI =          "doi:10.1016/j.chemolab.2016.03.025",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169743916300612",
  abstract =     "A potential alternative to cell batteries is the
                 air-breathing micro direct methanol fuel cell (muDMFC)
                 because it is environmental friendly, charging-free,
                 possesses high energy density properties and provides
                 easy storage of the fuel. The effective functioning of
                 the complex air-breathing uDMFC system exhibits higher
                 dependence on its operating conditions and the
                 parameters. The main challenge for the experts is to
                 determine its optimum operating conditions. In this
                 context, the mathematical modelling approach based on
                 evolutionary framework of genetic programming (GP) can
                 be applied. However, its successful implementation
                 depends on the complexity chosen in its structural risk
                 minimization (SRM) objective function. In this work,
                 the two measures of complexity based on the
                 standardized number of nodes and the number of basis
                 functions in the splines is chosen. Comparison between
                 the two GP approaches based on these two complexity
                 measures is evaluated on the experimental procedure
                 performed on the DMFC. The power characteristics
                 considered in this study are power density and
                 open-circuit voltage and the three inputs considered
                 are methanol flow rate, methanol concentration and the
                 cell temperature. The statistical analysis based on
                 cross-validation, error metrics and hypothesis tests is
                 performed to choose the best GP based power
                 characteristics models. Further, 2-D plots for
                 measuring the individual effects and the 3-D plots for
                 the interaction effects of the inputs on the power
                 characteristics is plotted based on the parametric
                 approach. It was found that the methanol concentration
                 influences the power characteristics (power density and
                 OCV) of DMFC the most followed by cell temperature and
                 methanol flow rate.",
  keywords =     "genetic algorithms, genetic programming, Direct
                 methanol fuel cell, DFMC, Fuel cell performance, Power
                 characteristics",
}

Genetic Programming entries for Akhil Garg Biranchi Narayan Panda D Y Zhao Kang Tai

Citations