Controller design using parametric neural networks

HashemiNejad, Mahmoud; Murata, Junichi; Hirasawa, K.

doi:10.1109/sice.1995.526694

Cited by 1 publication

(1 citation statement)

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“…Thus MPNN is somehow a gray box rather than a sole black box. Finally the transfer function used in the parametric neural network can be given by Equation ( 12) [52,53]. Equation ( 13) presents the Mean Squares error function.…”

Section: Parametric Neural Networkmentioning

confidence: 99%

Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network

Pourkiaei

Ahmadi

Hasheminejad

2015

Mechanics & Industry

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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.

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Section: Parametric Neural Networkmentioning

confidence: 99%