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
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;
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
DOI :
10.1109/ICCASM.2010.5619086
