A novel two stage algorithm for construction of RBF neural models based on A-optimality criterion

Deng, Jiankang; Li, Kang; Harkin‐Jones, Eileen; Fei, Minrui; Li, Shaoyuan

doi:10.1109/icnc.2013.6817933

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…TSS ALGORITHM According to [9], [10], [12], the RBF-NN structure makes it possible to formulate its construction as a linear-in-theparameters structure. Based on this formulation, a compact RBF-NN can be built using the two-stage stepwise identification method.…”

Section: Construction Of Compact Rbf Network Usingmentioning

confidence: 99%

Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization

Zhang

Yang

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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As a novel family member of the redox flow batteries (RFBs), the single flow zincnickel battery (ZNB) without ion exchange membranes has attracted a lot of interests in recent years due to the high charging and discharging efficiencies. To understand the electrical behaviour is a key for proper battery management system. Unlike the electrochemical mechanism models and equivalent circuit models, the neural network based blackbox model does not need knowledge about the electrochemical reactions and is a promising and adaptive approach for the ZNB battery modelling. In this paper, a compact radial basis function neural network is developed using a two-stage layer selection strategy to determine the network structure. While Jaya optimization is utilized to determine the non-linear parameters in the selected hidden nodes of the resultant RBF neural network (RBF-NN) model. The proposed method is implemented to model the ZNB to capture the non-linear electric behaviours through the readily measurable input signals. Experimental results manifest the accurate prediction capability of the resultant neural model and confirm the effectiveness of the proposed approach.

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Section: Construction Of Compact Rbf Network Usingmentioning

confidence: 99%

Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization

Zhang

Yang

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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“…To overcome some shortcomings in the aforementioned methods for the battery pack SOC estimation, this paper presents an improved RBF method using a fast recursive algorithm (FRA) to estimate the SOC of a battery pack. e FRA method [27] can be used for both neural inputs selection [28] and hidden layer node selection [29][30][31] in the configuration of RBF networks. Comparing to [32], the average cell temperature, the time mean pack voltage, the time mean pack temperature, and the time mean loop current all over 10 seconds intervals can be also added to the initial candidate pool of input variables, other input candidates can also be included such as the maximum cell voltage, the minimum cell voltage, the average cell voltage, and loop current.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Zhang

Zheng

et al. 2020

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Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

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State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF

Zhang

et al. 2017

Communications in Computer and Information Science

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A novel two stage algorithm for construction of RBF neural models based on A-optimality criterion

Cited by 4 publications

References 21 publications

Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization

Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization

State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF

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