A merged fuzzy neural network and its applications in battery state-of-charge estimation
學年 96
學期 1
出版（發表）日期 2007-08-20
作品名稱 A merged fuzzy neural network and its applications in battery state-of-charge estimation
作品名稱（其他語言）
著者 I-Hsum Li; Wei-Yen Wang; Shun-Feng Su; Yuang-Shung Lee
單位
出版者
著錄名稱、卷期、頁數 IEEE Transactions on Energy Conversion 22(3) ,p.697-708
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.
關鍵字 Fuzzy neural networks;Batteries;State estimation;Function approximation;Fuzzy control;Genetic algorithms;Neural networks;Nonlinear systems;Spline;Fuzzy logic
語言 en
ISSN 0885-8969
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115951 )