A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation

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 comput...

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Bibliographic Details
Published in:IEEE transactions on energy conversion 2007-09, Vol.22 (3), p.697-708
Main Authors: Li, I-Hsum, Wang, Wei-Yen, Su, Shun-Feng, Lee, Yuang-Shung
Format: Article
Language:English
Subjects:
B-spline membership functions (BMFs)
Batteries
Battery
Battery state-of-charge (BSOC)
battery string
Electric batteries
Function approximation
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy neural networks
fuzzy neural networks (FNNs)
Fuzzy set theory
Genetic algorithms
Learning
Mathematical analysis
Mathematical models
merged-FNN
Neural networks
Nonlinear systems
reduced-form genetic algorithm (RGA)
Spline
State estimation
Studies
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Summary: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.
ISSN:0885-8969
1558-0059
DOI:10.1109/TEC.2007.895457