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A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries
Li, Xiang; Li, Kang; Yang, Zhile; Wong, Chikong; Li, K; Xue, Y; Cui, S; Niu, Q; Yang, Z; Luk, P
2017
Conference NameADVANCED COMPUTATIONAL METHODS IN ENERGY, POWER, ELECTRIC VEHICLES, AND THEIR INTEGRATION, LSMS 2017, PT 3
Volume763
Pages386-395
Publication PlaceHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
PublisherSPRINGER-VERLAG BERLIN
AbstractAs a popular type of Redox Flow Batteries (RFBs), single flow Zinc Nickel Battery (ZNB) was proposed in the last decade without requiring an expensive and complex ionic membrane in the battery. In this paper, a Radial Basis Function (RBF) neural model is proposed for modelling the behaviours of ZNBs. Both the linear and non-linear parameters in the model are tuned through a new feedback-learning phase assisted Teaching-Learning-Based Optimization (TLBO) method. Besides, the fast recursive algorithm (FRA) is applied to select the proper inputs and network structure to reduce the modelling error and computational efforts. The experimental results confirm that the proposed methods are capable of producing ZNB models with desirable performance over both training and test data.
KeywordZinc Nickel Batteries (ZNBs) Radial Basis Function (RBF) Teaching-Learning-Feedback-Based Optimization (TLFBO)
DOI10.1007/978-981-10-6364-0_39
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000446999900039
The Source to ArticleWOS
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Li, Xiang,Li, Kang,Yang, Zhile,et al. A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries[C]. HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY:SPRINGER-VERLAG BERLIN,2017:386-395.
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