教師資料查詢 | 類別: 期刊論文 | 教師: 李宜勳 LIN, I-HSUN (瀏覽個人網頁)

標題: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
語言英文
ISSN0885-8969
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,電子版
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