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

doi:10.1109/TEC.2007.895457

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115951

Title:	A merged fuzzy neural network and its applications in battery state-of-charge estimation
Authors:	Li, I-Hsum;Wang, Wei-Yen;Su, Shun-Feng;Lee, Yuang-Shung
Keywords:	Fuzzy neural networks;Batteries;State estimation;Function approximation;Fuzzy control;Genetic algorithms;Neural networks;Nonlinear systems;Spline;Fuzzy logic
Date:	2007-08-20
Issue Date:	2019-03-13 12:10:20 (UTC+8)
Publisher:	IEEE
Abstract:	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.
Relation:	IEEE Transactions on Energy Conversion 22(3) ,p.697-708
DOI:	10.1109/TEC.2007.895457
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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