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

被引：3
作者：
Department of Mathematics and Computer Science, University of Missouri - St. Louis, One University Blvd., St. Louis, MO 63121, United States [1 ]
机构：
[1] Department of Mathematics and Computer Science, University of Missouri - St. Louis, St. Louis, MO 63121, One University Blvd.
来源：
Int. J. Inf. Commun. Technol. | 2008年 / 3-4卷 / 453-461期
关键词：
Genetic programming; SOFC stack; Solid oxide fuel cell;
D O I：
10.1504/IJICT.2008.024015
中图分类号：
学科分类号：
摘要：
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. Copyright © 2008, Inderscience Publishers.
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页码：453 / 461
页数：8
相关论文
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