.png){kind=icon}
返回
An optimal model identification for solid oxide fuel cell based on extreme learning machines optimized by improved Red Fox Optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijhydene.2021.06.046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The present study proposes an efficient method for optimal model parameters estimation of the Solid Oxide Fuel Cell by considering its nonlinear dynamic behavior. The approach is relied on using a novel optimal model of Extreme Learning Machines (ELM) network based on metaheuristics. The main purpose is to minimize the Mean Squared Error (MSE) be-tween the empirical output voltage data and the output voltage of the model by the sug-gested optimized ELM network. The proposed ELM network is optimized by an enhanced design of the Red Fox Optimizer (IRFO) Algorithm to provide optimal results. The suggested ELM-IRFO method is then testified on a Solid Oxide Fuel Cell case study and its results are compared with the GWO-RHNN method from the literature and ELM-RFO method to show its effectiveness. The final results showed that the proposed ELM-IRFO has the minimum value of the Mean Squared Error (MSE) against the other comparative methods. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Keyword:
Extreme learning machines
Model identification
Output voltage
Solid oxide fuel cell
Improved red fox optimization algorithm
期刊
IF:
8.3
论文数:
5.5W
被引数:
23.1W
机构
引用论文
Gelatin-assisted fabrication of water-dispersible graphene and its inorganic analogues明胶辅助制备水分散性石墨烯及其无机类似物
PREAI
分享收藏Influence of Temperature on Vibrational Frequency of Graphene Sheet Used as Nano-Scale Sensing.png)
C
OAAI
CIF0
OAAI分享收藏Development and validation of the Detail and Flexibility Questionnaire (DFlex) in eating disorders
PREAI
分享收藏Wafer-Scale Growth of Single-Crystal Monolayer Graphene on Reusable Hydrogen-Terminated Germanium可重复使用的氢封端锗上单晶单层石墨烯的晶圆级生长Science
ScienceIF0
PREAI
分享收藏
分享收藏
查看更多内容