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Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling.

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Title: Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling.
Authors: Rezazadeh, S.1 sor.mems@gmail.com, Mehrabi, M.2, Pashaee, T.3, Mirzaee, I.1
Source: Journal of Mechanical Science & Technology. Nov2012, Vol. 26 Issue 11, p3701-3709. 9p.
Subjects: Adaptive fuzzy control, Proton exchange membrane fuel cells, Humidity, Numerical analysis, Current density (Electromagnetism), Pressure, Air
Abstract: In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm) is modeled to the variation of pressure at the cathode side P (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient α, relative humidity of inlet fuel RH and relative humidity of inlet air RH which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical-validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm) is modeled to the variation of pressure at the cathode side P (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient α, relative humidity of inlet fuel RH and relative humidity of inlet air RH which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical-validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states. [ABSTRACT FROM AUTHOR]
ISSN:1738494X
DOI:10.1007/s12206-012-0844-2