摘要
采用基于改进粒子群算法的BP神经网络(Improved-PSO BPNN),建立质子交换膜燃料电池(PEMFC)电特性模型。PEMFC系统仿真结果表明该方法简单、有效、精度高,与采用传统BP神经网络的模型相比具有明显的优越性,为PEMFC系统建模,电池性能优化以及控制系统设计提供了新的思路。
BP neural network based on modified particle swarm optimization algorithm （Improved - PSO BPNN） which has favorable regression capability for the given specimen data to build an electric characteristic model of PEMFC was adopted. The results showed that the method was simple, effective, and precise. It provides a novel method for establishing the PEMFC model and improving the performance of cells as well as to design controlling systems.
出处
《应用能源技术》
2009年第5期1-4,共4页
Applied Energy Technology
关键词
质子交换膜燃料电池
非线性系统建模
神经网络
粒子群算法
proton exchange membrane fuel cell
nonlinear system modeling
artificial neural network
particle swarm optimization algorithm
