Performance evaluation of microbial fuel cell by artificial intelligence methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Garg:2014:ESA,
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author = "A. Garg and V. Vijayaraghavan and S. S. Mahapatra and
K. Tai and C. H. Wong",
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title = "Performance evaluation of microbial fuel cell by
artificial intelligence methods",
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journal = "Expert Systems with Applications",
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volume = "41",
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number = "4, Part 1",
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pages = "1389--1399",
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year = "2014",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2013.08.038",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417413006507",
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keywords = "genetic algorithms, genetic programming, MFC
modelling, MFC prediction, GPTIPS, LS-SVM",
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abstract = "In the present study, performance of microbial fuel
cell (MFC) has been modelled using three potential
artificial intelligence (AI) methods such as multi-gene
genetic programming (MGGP), artificial neural network
and support vector regression. The effect of two input
factors namely, temperature and ferrous sulfate
concentrations on the output voltage were studied
independently during two operating conditions (before
and after start-up) using the three AI models. The data
is randomly divided into training and testing samples
containing 80percent and 20percent sets respectively
and then trained and tested by three AI models. Based
on the input factor, the proposed AI models predict
output voltage of MFC at two operating conditions. Out
of three methods, the MGGP method not only evolve model
with better generalisation ability but also represents
an explicit relationship between the output voltage and
input factors of MFC. The models generated by MGGP
approach have shown an excellent potential to predict
the performance of MFC and can be used to gain better
insights into the performance of MFC.",
- }
Genetic Programming entries for
Akhil Garg
Venkatesh Vijayaraghavan
Siba Sankar Mahapatra
Kang Tai
Chee How Wong
Citations