Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell
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- @Article{Garg:2016:CILS,
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author = "Akhil Garg and B. N. Panda and D. Y. Zhao and K. Tai",
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title = "Framework based on number of basis functions
complexity measure in investigation of the power
characteristics of direct methanol fuel cell",
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journal = "Chemometrics and Intelligent Laboratory Systems",
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volume = "155",
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pages = "7--18",
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year = "2016",
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ISSN = "0169-7439",
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DOI = "doi:10.1016/j.chemolab.2016.03.025",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169743916300612",
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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.",
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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