Nonlinear modeling of MCFC stack based on RBF neural networks identification

被引：30
|
作者：
Shen, C [1 ]
Cao, GY [1 ]
Zhu, XJ [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Automat, Inst Fuel Cell, Shanghai 200030, Peoples R China
来源：
SIMULATION MODELLING PRACTICE AND THEORY | 2002年 / 10卷 / 1-2期
基金：
上海市科技启明星计划;
关键词：
molten carbonate fuel cells; radial basis function; modeling; neural networks; identification;
D O I：
10.1016/S1569-190X(02)00064-3
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Modeling molten carbonate fuel cells (MCFC) is very difficult and the most existing models are based on conversation laws which are too complicated to be used to design a control system. This paper presents an application of radial basis functions (RBF) neural networks identification to develop a nonlinear temperature model of MCFC stack. The temperature characters of MCFC stack are briefly analyzed. A summary of RBF neural networks modeling of MCFC is introduced. The simulation tests reveal that it is feasible to establish the model of MCFC stack using RBF neural networks identification. The modeling process avoids using complicated differential equations to describe the stack and the neural networks model developed can be used to predict the temperature responses online which makes it possible to design online controller of MCFC stack. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：109 / 119
页数：11
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