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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Veröffentlicht in:	International journal of energy research 2021-10, Vol.45 (12), p.17903-17923
Hauptverfasser:	Yang, Bo, Chen, Yijun, Guo, Zhengxun, Wang, Jingbo, Zeng, Chunyuan, Li, Danyang, Shu, Hongchun, Shan, Jieshan, Fu, Ting, Zhang, Xiaoshun
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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