DocumentCode
3592922
Title
Application of neural network based on improved Ant Colony Optimization in soft sensor modeling of polymer electrolyte membrane moisture
Author
Li Xin ; Qun, Yan ; Yu DaTai
Author_Institution
Inf. Eng. Sch., Univ. of Sci. & Technol. in Beijing, Beijing, China
Volume
8
fYear
2010
Abstract
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.
Keywords
computerised instrumentation; moisture measurement; neural nets; optimisation; power engineering computing; proton exchange membrane fuel cells; sensors; ant colony optimization; artificial neural network; moisture measurement; polymer electrolyte membrane fuel cell; soft sensor modeling; Artificial neural networks; Biomembranes; Frequency modulation; Moisture; Moisture measurement; PEMFC; ant colony optimization; artificial neural network; moisture; soft sensor;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Print_ISBN
978-1-4244-7235-2
Electronic_ISBN
978-1-4244-7237-6
Type
conf
DOI
10.1109/ICCASM.2010.5619086
Filename
5619086
Link To Document