We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network.
- Authors
MOHSEN POURKIAEI, SEYED; AHMADI, MOHAMMAD HOSSEIN; HASHEMINEJAD, S. MAHMOUD
- Abstract
In this paper two artificial intelligence techniques to predict and control behavior of a 25W fabricated proton exchange membrane (PEM) fuel cell, have been investigated. These approaches are: "Parametric Neural Network (PNN)" and "Group Method of Data Handling (GMDH)" for the first time. A PNN model is developed by introducing a "p" parameter in the activation function of the neural network. PNN model with its specific tangent hyperbolic transfer function have the ability to be with different nonlinearity degrees of input data. To develop GMDH network, quadratic polynomial was utilized. To determine proper weights of GMDH network, back propagation algorithm has been used. The input layer consists of gas pressure, fuel cell temperature and input current experimental data, to predict the output voltage. The results show that both generalized Parametric and GMDH-type neural networks are reliable tools to predict the output voltage of PEM fuel cell with high coefficient of determination values of 0.96 and 0.98.
- Subjects
ARTIFICIAL intelligence; NEURAL circuitry; DIRECT energy conversion; POWER resources; FUEL quality
- Publication
Mechanics & Industry, 2016, Vol 17, Issue 1, p105-1
- ISSN
2257-7777
- Publication type
Article
- DOI
10.1051/meca/2015050