Performance evaluation of microbial fuel cell by artificial intelligence methods

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@Article{Garg:2014:ESA,
  author =       "A. Garg and V. Vijayaraghavan and S. S. Mahapatra and 
                 K. Tai and C. H. Wong",
  title =        "Performance evaluation of microbial fuel cell by
                 artificial intelligence methods",
  journal =      "Expert Systems with Applications",
  volume =       "41",
  number =       "4, Part 1",
  pages =        "1389--1399",
  year =         "2014",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2013.08.038",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417413006507",
  keywords =     "genetic algorithms, genetic programming, MFC
                 modelling, MFC prediction, GPTIPS, LS-SVM",
  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