2007
DOI: 10.1109/tec.2007.895457
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A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation

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 po… Show more

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Cited by 159 publications
(57 citation statements)
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“…The solutions make use of direct measurements and/ or coulomb-counting, as well as computation intelligence techniques such as Fuzzy logic (e.g., [88], [89]), artificial neural networks (e.g., [88], [90], [91]), or Kalman filters (e.g., [91], [92]). Due to their selflearning ability, the adaptive systems are capable to draw useful conclusions from ambiguous or imprecise data with a multitude of variables, and in this way to respond to the timevarying behaviour of batteries and users.…”
Section: Kalman Filtersmentioning
confidence: 99%
“…The solutions make use of direct measurements and/ or coulomb-counting, as well as computation intelligence techniques such as Fuzzy logic (e.g., [88], [89]), artificial neural networks (e.g., [88], [90], [91]), or Kalman filters (e.g., [91], [92]). Due to their selflearning ability, the adaptive systems are capable to draw useful conclusions from ambiguous or imprecise data with a multitude of variables, and in this way to respond to the timevarying behaviour of batteries and users.…”
Section: Kalman Filtersmentioning
confidence: 99%
“…To avoid the difficulty of battery modeling and identification, machine learning strategies were also introduced to establish black-boxes mapping measurable data to SoC, including Neural Network (NN) [21], fuzzy NN [22,23], evolutionary NN [24,25] and support vector machine [26,27]. These data-oriented methods can not avoid their intrinsic problems such as large number of training data covering the whole possible range of operation, the selection of model structure and the balance between under-fitting and over-fitting.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In previous research, artificial neural networks have been widely used to estimate, evaluate, and predict results based on input data [31]. In battery research, this method has been employed to estimate battery SOC [32][33][34][35][36][37][38], state of health [39], and surface temperature [40].…”
Section: Introductionmentioning
confidence: 99%