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NNARX Model of the PEMFC Used Neural Network Pruning Model Structure

Shan-Jen Cheng 1, Jr-Ming Miao 2, and Te-Jen Chang 3
1. Department of Aircraft Engineering, Army Academy ROC, Taiwan (R.O.C.)
2. Department of Biomechantronics Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan, R.O.C.
3. Department of Electrical Engineering, Chung Cheng Institute of Technology, National Defense University, Taiwan (R.O.C.)

Abstract—The paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-Regressive model with eXogenous inputs (NNARX) approach. The Multilayer Perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The NNARX model structure is according to the Optimal Brain Surgeon (OBS) methodology to indicate the significant network structure. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model based on OBS technique can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Index Terms—PEMFC, NNARX, Optimal Brain Surgeon (OBS), Neural Network (NN)

Cite: Shan-Jen Cheng, Jr-Ming Miao. and Te-Jen Chang, "NNARX Model of the PEMFC Used Neural Network Pruning Model Structure," International Journal of Electrical Energy, Vol. 4, No. 1, pp. 1-5, March 2016. doi: 10.18178/ijoee.4.1.1-5

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