文献类型：期刊文献

英文题名：Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm

作者：Jia, Hailong[1];Taheri, Bahman[2]

第一作者：贾海龙

通讯作者：Jia, HL[1]

机构：[1]Xinxiang Univ, Informt Management Ctr, Xinxiang 453000, Henan, Peoples R China;[2]Islamic Azad Univ, Ardabil Branch, Young Researchers & Elite Club, Ardebil, Iran

第一机构：新乡学院

通讯机构：[1]corresponding author), Xinxiang Univ, Informt Management Ctr, Xinxiang 453000, Henan, Peoples R China.|[11071]新乡学院;

年份：2021

卷号：7

起止页码：3328-3337

外文期刊名：ENERGY REPORTS

收录：;EI(收录号：20212510539101);Scopus(收录号：2-s2.0-85108181590);WOS:【SCI-EXPANDED(收录号:WOS:000701667500017)】；

基金：This work was supported by Scientific and technological project of Henan provincial science and technology department, China (NO. 182102210495)

语种：英文

外文关键词：Solid Oxide Fuel Cell; Modeling; Elman Neural Network; Quantum Pathfinder (QPF) algorithm; Output voltage

摘要：In this research, a new efficient method is introduced for model assessment of Solid Oxide Fuel Cell (SOFC) model using a new hybrid Elman Neural Network (ENN). The main purpose of this research is to minimize the Mean Squared Error (MSE) between empirical data and modeling data of the fuel cell output voltage using the suggested hybrid ENN. The designed ENN is indeed a combination of this network with an improved metaheuristic, called Quantum Pathfinder (QPF) algorithm to give an optimal model. The proposed QPF-ENN model is then performed in a SOFC case study to show its efficiency. The results of the suggested method are validated by the reference voltage and also two other methods to show the higher minimum value of the Mean Squared Error (MSE) toward the others. Simulation results are analyzed the mean squared error value of the methods for 5000 samples, where, the voltage is limited between 320 V and 361 V. The results show that the mean square error for the QPF-Elman method, GWO-RHNN method, and PF-Elman method are 0.0014, 0.0017, and 0.0018, respectively. This indicates that the proposed QPF-Elman delivers the minimum value of the mean square error. (C) 2021 The Authors. Published by Elsevier Ltd.