A merged fuzzy neural network and its applications in battery state-of-charge estimation

doi:10.1109/TEC.2007.895457

淡江大學機構典藏 > 工學院 > 機械與機電工程學系暨研究所 > 期刊論文 > Item 987654321/115951

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题名:	A merged fuzzy neural network and its applications in battery state-of-charge estimation
作者:	Li, I-Hsum;Wang, Wei-Yen;Su, Shun-Feng;Lee, Yuang-Shung
关键词:	Fuzzy neural networks;Batteries;State estimation;Function approximation;Fuzzy control;Genetic algorithms;Neural networks;Nonlinear systems;Spline;Fuzzy logic
日期:	2007-08-20
上传时间:	2019-03-13 12:10:20 (UTC+8)
出版者:	IEEE
摘要:	To solve learning problems with vast number of inputs, this paper proposes a novel learning structure merging a number of small fuzzy neural networks (FNNs) into a hierarchical learning structure called a merged-FNN. In this paper, the merged-FNN is proved to be a universal approximator. This computing approach uses a fusion of FNNs using B-spline membership functions (BMFs) with a reduced-form genetic algorithm (RGA). RGA is employed to tune all free parameters of the merged-FNN, including both the control points of the BMFs and the weights of the small FNNs. The merged-FNN can approximate a continuous nonlinear function to any desired degree of accuracy. For a practical application, a battery state-of-charge (BSOC) estimator, which is a twelve input, one output system, in a lithium-ion battery string is proposed to verify the effectiveness of the merged-FNN. From experimental results, the learning ability of the newly proposed merged-FNN with RGA is superior to that of the traditional neural networks with back-propagation learning.
關聯:	IEEE Transactions on Energy Conversion 22(3) ,p.697-708
DOI:	10.1109/TEC.2007.895457
显示于类别:	[機械與機電工程學系暨研究所] 期刊論文

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