| Nonlinear modelling of a SOFC stack by improved neural networks identification |
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| 作者姓名： | WU Xiao-juan ZHU Xin-jian CAO Guang-yi TU Heng-yong |
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| 作者单位： | Institute of Fuel Cell，Shanghai Jiao Tong University，Shanghai 200030，China |
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| 摘 要： | The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.
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| 关 键 词： | 固体氧化物燃料电池 径向基函数神经网络 遗传算法 非线性建模 |
| 收稿时间： | 22 December 2006 |
| 修稿时间： | 2006-12-222007-04-13 |
Nonlinear modelling of a SOFC stack by improved neural networks identification |
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| WU Xiao-juan ZHU Xin-jian CAO Guang-yi TU Heng-yong.Nonlinear modelling of a SOFC stack by improved neural networks identification[J].Journal of Zhejiang University Science,2007,8(9):1505-1509. |
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| Authors: | Xiao-juan Wu Xin-jian Zhu Guang-yi Cao Heng-yong Tu |
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| Institution: | (1) Institute of Fuel Cell, Shanghai Jiao Tong University, Shanghai, 200030, China |
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| Abstract: | The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model. |
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| Keywords: | Solid oxide fuel cells (SOFCs) Radial basis function (RBF) Neural networks Genetic algorithm (GA) |
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