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