Research on SOC fuzzy weighted algorithm based on GA‐BP neural network and ampere integral method

Guo, Na; Fang, Yu; Tian, Zhongli; Cao, Shuai

doi:10.1049/joe.2018.9385

Cited by 30 publications

(15 citation statements)

References 6 publications

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“…The T-S fuzzy structure has strong self-adaptive ability and has the characteristics of automatic update and fuzzy membership. According to the "if-then" rule, when the rule is R j , the corresponding fuzzy reasoning rule is as follows [32][33][34]:…”

Section: Information Fusion Of Grassroots Teaching Organizationmentioning

confidence: 99%

Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization

Zhao

2022

Wireless Communications and Mobile Computing

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At present, the existing teaching construction information fusion methods are based on the classification results of teaching resources, but ignoring the global intelligent optimization of the fusion results, which leads to the deviation of the grassroots teaching organization construction information fusion results, and the fusion index value is low. Therefore, the information fusion method of the grassroots teaching organization construction based on intelligent optimization is proposed. The basic framework of information fusion is designed, and the information fusion is divided into the data layer, characteristic layer, and decision layer. The information characteristics of the construction of grassroots teaching organizations were extracted. The particle swarm optimization algorithm is used to search the maximum value of the work entropy of teaching grassroots unit, and the threshold value of teacher construction and grassroots unit work is used to realize the information fusion of teaching grassroots unit construction. Experimental results show that the proposed method has higher error rate, stability, efficiency index, and higher information fusion value and accuracy than the traditional method.

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Section: Information Fusion Of Grassroots Teaching Organizationmentioning

confidence: 99%

Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization

Zhao

2022

Wireless Communications and Mobile Computing

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“…is not only achieves the complementary advantages of the two but also exerts the extensive nonlinear mapping ability of the neural network and the global search ability of the genetic algorithm. It accelerates the network learning speed and improves the approximation ability and generalization ability in the whole learning project [22].…”

Section: Ga-bp Temperature Compensation Modelmentioning

confidence: 99%

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Shen

Cao

et al. 2019

Shock and Vibration

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In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer.

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“…At present, common machine learning methods include the radial basis function neural network (RBFNN) and back propagation neural networks (BPNN). Due to its simple structure, the neural network has been widely used [18][19][20][21][22][23][24]. However, the model training time of these methods is long and the dependence on the amount of data is great.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

Wang

et al. 2021

IEEE Access

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An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.

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Research on SOC fuzzy weighted algorithm based on GA‐BP neural network and ampere integral method

Cited by 30 publications

References 6 publications

Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization

Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

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