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来源：百度知道 编辑：UC知道 时间：2024/05/21 14:01:51
Modeling a SOFC stack based on GA-RBF neural networks identification
Abstract
In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF)neural network based on a genetic algorithm (GA). During the process of modeling, 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. Furthermore, we utilize the gradient descent learning algorithm to adjust the parameters. The validity and accuracy of modeling are tested by simulations. Besides, compared with the BP neural network approach, the simulation results show that the GA-RBF approach is superior to the conventional BP neural network in predicting the stack voltage with different temperature. So it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA.© 2007 Elsevier B.V. All rights reserved. Keywords: Sol
Abstract
In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF)neural network based on a genetic algorithm (GA). During the process of modeling, 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. Furthermore, we utilize the gradient descent learning algorithm to adjust the parameters. The validity and accuracy of modeling are tested by simulations. Besides, compared with the BP neural network approach, the simulation results show that the GA-RBF approach is superior to the conventional BP neural network in predicting the stack voltage with different temperature. So it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA.© 2007 Elsevier B.V. All rights reserved. Keywords: Sol
基于GA-RBF神经网络识别技术建模SOFC堆栈
摘要
本文给出了如何基于基因算法（GA）使用径向基函数（RBF）建立一个固体氧化物燃料电池（SOFC）的非线性离线模型。建模时，GA的目标是优化RBF神经网络参数，而优化值则作为RBF神经网络参数的初始值。而且，我们利用梯度下降学习算法调整这些参数。采用模拟方法来检测建模的正确性和准确度。另外，与BP神经网络方法相比，模拟结果显示，在不同温度下预测堆栈电压时使用GA-RBF方法优于传统的BP神经网络。因此使用基于GA的RBF神经网络识别方法建立SOFC堆栈模型是可行的。
© 2007 Elsevier B.V。版权所有。
关键字：固态氧化物燃料电池（SOFC），径向基函数（RBF），神经网络，基因算法，识别