A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell

Rezaei, Masoud; Mohseni, Mohammadreza

doi:10.1115/1.4023838

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…[33] and [35], which confirms what is already known about MLP network behavior, subsequently, this will confirm the fact that a pattern recognition problem of proton transfer could be well solved by using the feed-forward back-propagation neural network approach. In this regard, it is noteworthy to mention that this phenomenon has been the main topic of many previous studies based on developing such ANN applications, [33,[35][36][37][38][39][40][41][42][43][44] but each study has its particular objectives and it targets some specific chemical characteristics, usually other than those we have been adopted here (e.g, proton transfer rate) but all of these studies including the current study, have agreed on the importance in using the ANN neural network, for solving the problem of nonlinearity relationship between input variables and the required output. Therefore, the proposed tool is expected to be used as a supplementary manner in analyzing the proton transfer phenomena and would help researchers make their decisions toward this phenomenon using the EVB code based on automatical chart fitting and drawing of the large output file in one workstation via a flexible GUI.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

Tahat¹,

Rabassa²,

Khwaldeh³

et al. 2014

Chinese Phys. B

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In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

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