Genetic programming model of solid oxide fuel cell stack: first results

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Chakraborty:2008:IJICT,

• author = "Uday K. Chakraborty",
• title = "Genetic programming model of solid oxide fuel cell stack: first results",
• journal = "International Journal of Information and Communication Technology (IJICT)",
• year = "2008",
• volume = "1",
• number = "3/4",
• pages = "453--461",
• keywords = "genetic algorithms, genetic programming, solid oxide fuel cells, SOFC stack, modelling, nonlinear dynamics, simulation",
• publisher = "Inderscience Publishers",
• ISSN = "1741-8070",
• bibsource = "OAI-PMH server at www.inderscience.com",
• language = "eng",
• URL = "http://www.inderscience.com/link.php?id=24015",
• DOI = "doi:10.1504/IJICT.2008.024015",
• abstract = "Models that predict performance are important tools in understanding and designing solid oxide fuel cells (SOFCs). Modelling of SOFC stack-based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have already been reported for the modelling of solid oxide fuel cell stack-based systems. This paper presents a new, genetic programming approach to SOFC modelling. Initial simulation results obtained with the proposed approach outperform the state-of-the-art radial basis function neural network method for this task.",

}

Genetic Programming entries for Uday K Chakraborty

Citations