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

I. Hum Li, Wei Yen Wang, Shun Feng Su, Yuang Shung Lee

研究成果: 雜誌貢獻文章

116 引文 斯高帕斯(Scopus)

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.

原文英語
頁(從 - 到)697-708
頁數12
期刊IEEE Transactions on Energy Conversion
22
發行號3
DOIs
出版狀態已發佈 - 2007 九月 1

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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