Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

Ana Maria Amarillo Bertone, Jefferson Beethoven Martins, Keiji Yamanaka

Abstract


A fuzzy  identification of the dynamical system  model is developed upon a data generated by a software simulator of a hydrogen fuel cell. The data presents a black box  model, just composed by inputs and outputs, carry no  additional information, and showing a strong nonlinear behavior. The choice for a fuzzy identification is based on the data features, and the malleability of the mathematical fuzzy  technique. This approach allows to accomplish the objectives of the research, among which, the validation of the method for it used in other industrial problems.  The dynamic system identification process is performed using a fuzzy clustering through  the Gustafson and Kessel algorithm, and a Takagi Sugeno fuzzy inference method. Validation tests are performed  in terms of the 4-fold technique, confirming the lack of the data over-training. These  results make the fuzzy approach looks as a promising tool for black-box identification  of non linear dynamic systems.

Keywords


Hydrogen fuel cell, fuzzy clustering, identification of dynamical systems, Takagi Sugeno inference method

Full Text:

PDF

References


L. A.~Aguirre, ``Introduction to System Identification''. UFMG publisher, Belo Horizonte, 2007.

H.J. ~Avelar, ``Study and development of an energy system based in a hydrogen fuel cell for the power injection in electrical net'', PhD Thesis Faculty of Electrical Engineering, FEELT, Federal University of Uberlandia,MG, Brazil, 2012.

J.~Abonyi, R.~Babuska and F.~Szeifert, Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models, textit{IEEE Trans. on Systems, Man, and Cybernetics}, textbf{32}, No 5 (2002), 612-621.

R. ~Babuska, ``Neuro-Fuzzy Methods for Modeling and Identification'', Springer Verlag, 2002.

J.C.~Bezdek, ``Pattern Recognition with Fuzzy Objective Function Algorithms'', Plenum Press, New York, 1981.

I. G. Costa, F. A.T. de Carvalho, M; C.P. de Souto, Comparative analysis of clustering methods for gene expression time course

data, textit{Genetics and Molecular Biology}, textbf{27}, No 4 (2004), 623-631.

J.C. ~Dunn, A fuzzy relative of the ISODATA process and its use in detecting

compact well-separated clusters, textit{J. Cybern}, textbf{3}, No 3 (1974), 32-57.

D.E.~Gustafson and W.C.~Kessel, Fuzzy clustering with fuzzy covariance matrix, in ``Proceedings of the IEEE Control and Decision Conference'', pp. 761-766, 1979.

A.K.~Jain and R.C.~Dubes,``Algorithms for Clustering Data''. Prentice Hall, 1988.

J.B.~MacQueen, Some Methods for classification and Analysis of Multivariate Observations, in ``Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability'', University of California Press, pp. 281-297, 1967.

E.~Ruspini, Numerical methods for fuzzy clustering, textit{Inf. Sci.} textbf{2}, (1970), 319-350.

C. F. Schoenbein, Lecture of 13 March 1839, textit{Berichte der Verhandlungen der naturforschenden Gesellschaft in Basel}, textbf{4}, (1839), 52-55.

T. Takagi, M. Sugeno, Fuzzy Identification of Systems and Its Applications to Modeling and Control, textit{IEEE Transactions on Systems, Man, and Cybernetics}, textbf{15}, No 1 (1985), 116-132.

``Office of Energy Efficiency'' & Renewable Energy

Acess in November 2016:

http://energy.gov/eere/fuelcells/fuel-cells




DOI: https://doi.org/10.5540/tema.2017.018.03.405

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



TEMA - Trends in Applied and Computational Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)
ISSN: 1677-1966  (print version),  2179-8451  (online version)

Indexed in:

                        

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia