Optimization of precision molding process parameters of viscoelastic materials based on BP neural network improved by genetic algorithm

Huang, Shengzhou; Jiang, Chengwei; Tian, Zhenfu; Ren, Bowen; Tang, Yu; Xie, Fang-Lin; Zheng, Yufeng; Gao, Qing

doi:10.1016/j.mtcomm.2023.106149

Cited by 3 publications

(1 citation statement)

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“…GA can optimize the model parameters to achieve optimal prediction results. [32] In this study, GA was used to optimize the number of decision trees and maximum depth of decision trees in the RF to improve the accuracy of the model. When constructing the GA, each sample is regarded as a chromosome, and the parameter in each sample is a segment of the gene; therefore, it is necessary to code the sample first.…”

Section: Parameter Optimization Of Rfmentioning

confidence: 99%

Capacity Prediction Method of Lithium‐Ion Battery in Production Process Based on Improved Random Forest

Liu,

Tang,

Wang

et al. 2023

Energy Tech

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Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method requires considerable time and energy consumption; therefore, an accurate capacity estimation is crucial in reducing production costs. Herein, a capacity prediction method for lithium‐ion batteries based on improved random forest (RF) is proposed. This method extracts features from the voltage data of the entire formation process and the first 25% of the grading process, saving 56.7% of the energy consumption and 74.6% of the time in the grading process. The importance of these features is ranked by RF, the best feature subset is selected, and the RF algorithm with parameters optimized by the genetic algorithm is applied to establish a capacity prediction model. The results show that the proposed model can accurately predict the capacity, with root mean square error (RMSE) and mean absolute percentage error (MAPE) values of only 191.89 mAh and 0.13%, respectively. Compared with other regression algorithms, the model shows a lower RMSE and MAPE and a higher capacity prediction accuracy for low‐capacity cells.

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Section: Parameter Optimization Of Rfmentioning

confidence: 99%