Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells

Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny ob...

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Vydané v:	International journal of energy research Ročník 45; číslo 12; s. 17903 - 17923
Hlavní autori:	Yang, Bo, Chen, Yijun, Guo, Zhengxun, Wang, Jingbo, Zeng, Chunyuan, Li, Danyang, Shu, Hongchun, Shan, Jieshan, Fu, Ting, Zhang, Xiaoshun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Inc 10.10.2021
Predmet:	Accuracy Algorithms artificial neural network Artificial neural networks Back propagation Back propagation networks Cell culture Electrochemistry Extracellular matrix Fuel cells Fuel technology Heuristic methods Identification Identification methods Levenberg‐Marquardt backpropagation Mathematical models Neural networks Optimal control Optimization optimization methods Parameter estimation Parameter identification parameter identification/estimation Parameters solid oxide fuel cell Solid oxide fuel cells Stability
ISSN:	0363-907X, 1099-114X
On-line prístup:	Získať plný text
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