Modeling a SOFC stack based on GA-RBF neural networks identification

@inproceedings{Wu2007ModelingAS,
  title={Modeling a SOFC stack based on GA-RBF neural networks identification},
  author={Xiao-Juan Wu and Xin-Jian Zhu and Guang-Yi Cao and Heng-Yong Tu},
  year={2007}
}
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 n a genetic algorithm (GA). During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum alues are regarded as the initial values of the RBF neural network parameters. Furthermore, we utilize the gradient descent learning algorithm to djust the parameters. The validity and accuracy of modeling are… CONTINUE READING