Open Access
Mechanics & Industry
Volume 17, Number 1, 2016
Article Number 105
Number of page(s) 11
Published online 16 October 2015
  1. S.J. Peighambardoust, S. Rowshanzamir, M. Amjadi, Review of the proton exchange membranes for fuel cell applications, Int. J. Hydrogen Energy 35 (2010) 9349–9384 [CrossRef] [Google Scholar]
  2. M. Radulescu, V. Ayel, O. Lottin, M. Feidt, B. Antoine, C. Moyne, D. Le Noc, S. Le Doze, Natural gas electric generator powered by polymer exchange membrane fuel cell: Numerical model and experimental results, Energy Convers. Manage. 49 (2008) 326–335 [CrossRef] [Google Scholar]
  3. O. Erdinc, M. Uzunoglu, Recent trends in PEM fuel cell-powered hybrid systems: Investigation of application areas, design architectures and energy management approaches, Renew. Sustain. Energy Rev. 14 (2010) 2874–2884 [CrossRef] [Google Scholar]
  4. O.S. Suslu, I. Becerik, On-Board Fuel Processing for a Fuel Cell-Heat Engine Hybrid System, Energy and Fuels 23 (2009) 1858–1873 [CrossRef] [Google Scholar]
  5. M. Hu, A. Gu, M. Wang, X. Zhu, L. Yu, Three dimensional, two phase flow mathematical model for PEM fuel cell. Part I. Model development, Energy Convers. Manage. 45 (2004) 1861–1882 [CrossRef] [Google Scholar]
  6. M. Hu, A. Gu, M. Wang, X. Zhu, L. Yu, Three dimensional, two phase flow mathematical model for PEM fuel cell. Part II. Analysis and discussion of the internal transport mechanisms, Energy Convers. Manage. 45 (2004) 1883–1916 [CrossRef] [Google Scholar]
  7. G. Hu, J. Fan, A Three-Dimensional, Multicomponent, Two-Phase Model for a Proton Exchange Membrane Fuel Cell with Straight Channels, Energy and Fuels 20 (2006) 738–747 [CrossRef] [Google Scholar]
  8. A.R.M. Sadiq Al-Baghdadi, H.A.K. Shahad Al-Janabi, Influence of the Design Parameters in a Proton Exchange Membrane (PEM) Fuel Cell on the Mechanical Behavior of the Polymer Membrane, Energy and Fuels 21 (2007) 2258–2267 [CrossRef] [Google Scholar]
  9. M. De Falco, Ethanol membrane reformer and PEMFC system for automotive application, Fuel 90 (2011) 739–747 [CrossRef] [Google Scholar]
  10. X.-D. Wang, W.-M. Yan, Y.-Y. Duan, F.-B. Weng, G.-B. Jung, C.-Y. Lee, Numerical study on channel size effect for proton exchange membrane fuel cell with serpentine flow field, Energy Convers. Manage. 51 (2010) 959–968 [CrossRef] [Google Scholar]
  11. M. Uzunoglu, M.S. Alam, Dynamic modeling, design and simulation of a PEM fuel cell/ultra-capacitor hybrid system for vehicular applications, Energy Convers. Manage. 48 (2007) 1544–1553 [CrossRef] [Google Scholar]
  12. P. Corbo, F.E. Corcione, F. Migliardini, O. Veneri, Energy management in fuel cell power trains, Energy Convers. Manage. 47 (2006) 3255–3271 [CrossRef] [Google Scholar]
  13. R.N. Methekar, V. Prasad, R.D. Gudi, Dynamic analysis and linear control strategies for proton exchange membrane fuel cell using a distributed parameter model, Power Sources 165 (2007) 152–170 [CrossRef] [Google Scholar]
  14. J.J. Baschuk, Li. Xianguo, Modeling of polymer electrolyte membrane fuel cells with variable degrees of water flooding, Power Sources 86 (2000) 181–196 [CrossRef] [Google Scholar]
  15. T.V. Nguyen, R.E. White, A Water and Heat Management Model for Proton-Exchange, J. Electrochem. Soc. 140 (1993) 2178–2186 [CrossRef] [Google Scholar]
  16. T.F. Fuller, J. Newman, Water and Thermal Management in Solid-Polymer-Electrolyte Fuel cell, J. Electrochem. Soc. 140 (1993) 1218–1225 [CrossRef] [Google Scholar]
  17. D.M. Bernardi, M.W. Verbrugge, A mathematical model of the solid-polymer-electrolyte fuel cell, J. Electrochem. Soc. 139 (1992) 2477–2491 [CrossRef] [Google Scholar]
  18. Z.H. Wang, C.Y. Wang, K.S. Chen, Two-phase flow and transport in the air cathode of proton exchange membrane fuel cells, J. Power Sources 94 (2001) 40–50 [CrossRef] [Google Scholar]
  19. C.Y. Du, P.F. Shi, J. Harbin, Multi-input and multi-output neural model of the mechanical nonlinear behavior of a PEM fuel cell system, Inst. Technol. 38 (2006) 1511–1514 [Google Scholar]
  20. R.P. Pathapati, X. Xue, J. Tang, A new dynamic model for predicting transient phenomena in a PEM fuel cell system, Renewable Energy 30 (2005) 1–22 [CrossRef] [Google Scholar]
  21. F. Mueller, J. Brouwer, S. Kang, H. Kim, K. Min, Quasi-three dimensional dynamic model of a proton exchange membrane fuel cell for system and controls development, J. Power Sources 163 (2007) 814–829 [CrossRef] [Google Scholar]
  22. Y. Shan, S.Y. Choe, A high dynamic PEM fuel cell model with temperature effects, J. Power Sources 145 (2005) 30–39 [CrossRef] [Google Scholar]
  23. W. Na, B. Gou, B. Diong, Nonlinear control of PEM fuel cells by exact linearization, Proc. IEEE Ind. Appl. Conf. 4 (2005) 2937–2943 [Google Scholar]
  24. A. Nasiri, V.S. Rimmalapudi, A. Emadi, D.J. Chmielewski, S. Al-Hallaj, Active control of a hybrid fuel cell-battery system, Power Electronics and Motion Control Conference, 2004 [Google Scholar]
  25. D.E. Adams, R.J. Randall, Neural model of the dynamic behaviour of a non-linear mechanical system, Proceedings of the 23rd International Conference on Noise and Vibration Engineering ISMA, 1998, pp. 517–529 [Google Scholar]
  26. S.O.T. Ogaji, R. Singh, P. Pilidis, M. Diacakis, Modeling fuel cell performance using artificial intelligence, J. Power Sources 154 (2006) 192–197 [CrossRef] [Google Scholar]
  27. S. Ou, L.E.K. Achenie, A hybrid neural network model for PEM fuel cells, J. Power Sources 140 (2005) 319–330 [CrossRef] [Google Scholar]
  28. A.U. Chávez-Ramírez, R. Muñoz-Guerrero, S.M. Durón-Torres, M. Ferraro, G. Brunaccini, F. Sergi, V. Antonucci, High power fuel cell simulator based on artificial neural network, Int. J. Hydrogen Energy 35 (2010) 12125–12133 [CrossRef] [Google Scholar]
  29. N.S. Sisworahardjo, T. Yalcinoz, Neural network model of 100 W portable PEM fuel cell and experimental verification, Int. J. Hydrogen Energy 35 (2010) 9104–9109 [CrossRef] [Google Scholar]
  30. K.Y. Chang, The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks, Int. J. Hydrogen Energy 36 (2011) 13683–13694 [CrossRef] [Google Scholar]
  31. D. Paclisan, W. Charon, Real time modeling of the dynamic mechanical behaviour of PEMFC thanks to neural networks, Eng. Appl. Artif. Intell. 26 (2013) 706–713 [CrossRef] [Google Scholar]
  32. J. Kim, I. Lee, State-of-health diagnosis based on hamming neural network using output voltage pattern recognition for a PEM fuel cell, Int. J. Hydrogen Energy 37 (2012) 4280–4289 [CrossRef] [Google Scholar]
  33. E. Sanchez, T. Shibata, L.A. Zadeh, Genetic algorithms and fuzzy logic systems, World Scientific, River edge NJ, 1997 [Google Scholar]
  34. K. Kristinson, G. Dumont, System identification and control using genetic algorithms, J. IEEE Trans. Syst. Man Cybern 22 (1992) 1033–1046 [CrossRef] [Google Scholar]
  35. T. Somayeh, M.H. Ahmadi, A. Kasaeian, A.H. Mohammadi, Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine. International Journal of Ambient Energy ahead-of-print (2014) 1-13, DOI:10.1080/01430750.2014.986289 [Google Scholar]
  36. M.H. Ahmadi, M. Mehrpooya, N. Khalilpoor, Artificial neural networks modelling of the performance parameters of the Stirling engine. International Journal of Ambient Energy ahead-of-print (2014) 1-7, DOI:10.1080/01430750.2014.964370 [Google Scholar]
  37. M.H. Ahmadi, S. Sorouri Ghare Aghaj, A. Nazeri, Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization. Neural Computing and Applications 22 (2013) 1141–1150 [Google Scholar]
  38. A.G. Ivakhnenko, Polynomial Theory of Complex Systems, IEEE Trans. Syst. Man Cybern SMC-1 (1971) 364–378 [Google Scholar]
  39. S.J. Farlow Self-organizing method in modelling: GMDH type algorithm, Marcel Dekker Inc, 1984 [Google Scholar]
  40. J.A. Mueller, F. Lemke, Self-organizing data mining: an intelligent approach to extract knowledge from data, Pub. Libri, Hamburg, 2000 [Google Scholar]
  41. M.H. Ahmadi, M.A. Ahmadi, M. Mehrpooya, M.A. Rosen, Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine. Sustainability 7 (2015) 2243–2255 [CrossRef] [Google Scholar]
  42. C.M. Fonseca, P.J. Fleming, Nonlinear system identification with multi-objective genetic algorithm, Proceedings of the 13th World congress of the international federation of automatic control, San Francisco, Pergamon Press, California, 1996, pp. 187–192 [Google Scholar]
  43. G.P. Liu, V. Kadirkamanathan, Multi-objective criteria for neural networkstructure selection and identification of nonlinear systems using genetic algorithms, IEEE Proc. Control Theory Appl. 146 (1999) 373–382 [CrossRef] [Google Scholar]
  44. N. Nariman-Zadeh, A. Darvizeh, R. Ahmad-Zadeh, Hybrid Genetic Design of GMDH-Type Neural Networks Using Singular Value Decomposition for Modelling and Prediction of the Explosive Cutting Process, J. Eng. Manufact. 217 (2003) 779–790 [CrossRef] [Google Scholar]
  45. V.W. Porto, Evolutionary computation approaches to solving problems in neural computation. In: T. Back, D.B. Fogel, Z. Michalewicz (eds.), Handbook of evolutionary computation, Institute of Physics Publishing and Oxford University Press, New York, 1997, pp. 1–6 [Google Scholar]
  46. X. Yao, Evolving artificial neural networks, Proc. IEEE 87 (1999) 1423–1447 [CrossRef] [Google Scholar]
  47. E.F. Vasechkina, V.D. Yarin, Evolving polynomial neural network by means of genetic algorithm: some application examples, 2001, Complex Int 9 [Google Scholar]
  48. X. Yao, Evolving Artificial Neural Networks, Proc. IEEE 87 (1999) 1423–1447 [CrossRef] [Google Scholar]
  49. N. Nariman-zadeh, K. Atashkari, A. Jamali, A. Pilechi, X. Yao, Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms, J. Eng. Optim. 37 (2005) 437–462 [CrossRef] [Google Scholar]
  50. S.S. Haykin, Neural Networks A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999 [Google Scholar]
  51. P.D. Wasserman, Neural computing theory and practice, Van Nostrand Reinhold, New York, 1989 [Google Scholar]
  52. M. Hasheminejad, J. Murata, K. Hirasawa, System Identification Using Neural Networks with parametric Sigmoid Functions, Trans. Soc. Instrum. Control Eng. 31 (1995) 277–283 [CrossRef] [Google Scholar]
  53. M. Hasheminejad, J. Murata, K. Hirasawa, Control Design Using Parametric Neural Networks, Trans. Soc. Instrum. Control Eng., 1995 [Google Scholar]
  54. K. Atashkari, N. Nariman-Zadeh, A. Jamali, A. Pilechi, Thermodynamic Pareto optimization of turbojet using multi-objective genetic algorithm, Int. J. Thermal Sci. 44 (2005) 1061–1071 [CrossRef] [Google Scholar]
  55. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali, A. Jamali, Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms, Energy Convers. Manage. 48 (2007) 1029–1041 [CrossRef] [Google Scholar]
  56. A. Jamali, N. Nariman-Zadeh, A. Darvizeh, A. Masoumi, S. Hamrang, Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process, Eng. Appl. Artif. Intell. 22 (2009) 676–687 [CrossRef] [Google Scholar]
  57. M.H. Ahmadi, M.A. Ahmadi, S.A. Sadatsakkak, M. Feidt, Connectionist intelligent model estimates output power and torque of stirling engine. Renewable and Sustainable Energy Reviews 50 (2015) 871–883 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.