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. |
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| 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] |
| Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rezazadeh%2C+S%2E%22">Rezazadeh, S.</searchLink><relatesTo>1</relatesTo><i> sor.mems@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mehrabi%2C+M%2E%22">Mehrabi, M.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Pashaee%2C+T%2E%22">Pashaee, T.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Mirzaee%2C+I%2E%22">Mirzaee, I.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Nov2012, Vol. 26 Issue 11, p3701-3709. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Adaptive+fuzzy+control%22">Adaptive fuzzy control</searchLink><br /><searchLink fieldCode="DE" term="%22Proton+exchange+membrane+fuel+cells%22">Proton exchange membrane fuel cells</searchLink><br /><searchLink fieldCode="DE" term="%22Humidity%22">Humidity</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Current+density+%28Electromagnetism%29%22">Current density (Electromagnetism)</searchLink><br /><searchLink fieldCode="DE" term="%22Pressure%22">Pressure</searchLink><br /><searchLink fieldCode="DE" term="%22Air%22">Air</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12206-012-0844-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 3701 Subjects: – SubjectFull: Adaptive fuzzy control Type: general – SubjectFull: Proton exchange membrane fuel cells Type: general – SubjectFull: Humidity Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Current density (Electromagnetism) Type: general – SubjectFull: Pressure Type: general – SubjectFull: Air Type: general Titles: – TitleFull: Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rezazadeh, S. – PersonEntity: Name: NameFull: Mehrabi, M. – PersonEntity: Name: NameFull: Pashaee, T. – PersonEntity: Name: NameFull: Mirzaee, I. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 1738494X Numbering: – Type: volume Value: 26 – Type: issue Value: 11 Titles: – TitleFull: Journal of Mechanical Science & Technology Type: main |
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