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.
Original language | English |
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Pages (from-to) | 697-708 |
Number of pages | 12 |
Journal | IEEE Transactions on Energy Conversion |
Volume | 22 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2007 Sep 1 |
Keywords
- B-spline membership functions (BMFs)
- Battery state-of-charge (BSOC)
- Battery string
- Fuzzy neural networks (FNNs)
- Merged-FNN
- Reduced-form genetic algorithm (RGA)
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering