Application of Neural Network based on Improved Ant Colony Optimization in Soft Sensor Modeling of Polymer Electrolyte Membrane Moisture

摘要：

In this paper, we established a soft sensor model to calculate the moisture of polymer electrolyte membrane fuel cells by artificial neural network. We trained ANN by an improved ant colony algorithm. Experimental tests indicate that the simulation results of PEMs moisture are very close to real values, and the method possesses high precision and speed and can meet actual demands. This soft sensor model can be applied in the control of PEMFCs moisture and temperature.

关键词： ant colony optimization PEMFC moisture soft sensor artificial neural network

作者: Li Xin Yan Qun Yu Datai

作者单位: Information Engineering School University of Science and Technology in Beijing Beijing, China Colleg Information Engineering School University of Science and Technology in Beijing Beijing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 629-633

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）