为解决锂离子电池荷电状态(state of charge,SOC)难以精确计算的难题,提出一种增强的混合蛙跳算法(mutation opposition shuffled frog-leaping algorithm,MOSFLA)优化快速学习网(fast learning network,FLN)的SOC预测模型。在混合蛙跳...为解决锂离子电池荷电状态(state of charge,SOC)难以精确计算的难题,提出一种增强的混合蛙跳算法(mutation opposition shuffled frog-leaping algorithm,MOSFLA)优化快速学习网(fast learning network,FLN)的SOC预测模型。在混合蛙跳算法中引入几何中心变异策略和反学习策略增强算法的全局优化性能;为改善FLN的预测性能,采用MOSFLA优化FLN模型参数并建立MOSFLN-FLN模型;利用该模型对电池SOC进行预测,并将预测结果与其他模型预测结果相比较。结果显示,MOSFLA-FLN绝对误差不超过2.71,预测精度高,为SOC的精确计算提供了一种有效方法。展开更多
Background： Geometric methods provide an analysis of autonomic modulation using the geometric properties of the resulting pattern, and represent an interesting tool in the analysis of heart rate variability （HRV）. ...Background： Geometric methods provide an analysis of autonomic modulation using the geometric properties of the resulting pattern, and represent an interesting tool in the analysis of heart rate variability （HRV）. The aim of this study was to evaluate the impact of functional training on cardiac autonomic modulation in healthy young women using the geometric indices of HRV. Methods： Data were analyzed from 29 women, and were stratified into a functional training group （FTG, n = 13; 23.00 ± 2.51 years; 21.90± 2.82 kg/m2） and a control group （CG, n = 16; 20.56 ± 1.03 years; 22.12±3.86 kg/m2）. The FTG received periodized functional training for 12 weeks. The cardiac autonomic modulation of both groups was evaluated before and after this training, and a qualitative analysis was performed using the Poincar6 plot. Results： There was a significant increase in the difference of the triangular index （RRTri）, SDI, SD2, and RR intervals in the FTG as compared to the CG, and the qualitative analysis from the Poincar6 plot showed an increase in the dispersion of beat-to-beat and long-term RR intervals in the functional group after training. No changes were observed in the triangular interpolation of RR interval histogram （TINN） or SD1/SD2. Conclusion： Functional training had a beneficial impact on autonomic modulation, as characterized by increased parasympathetic activity and overall variability, thus highlighting the clinical usefulness of this type of training.展开更多
文摘为解决锂离子电池荷电状态(state of charge,SOC)难以精确计算的难题,提出一种增强的混合蛙跳算法(mutation opposition shuffled frog-leaping algorithm,MOSFLA)优化快速学习网(fast learning network,FLN)的SOC预测模型。在混合蛙跳算法中引入几何中心变异策略和反学习策略增强算法的全局优化性能;为改善FLN的预测性能,采用MOSFLA优化FLN模型参数并建立MOSFLN-FLN模型;利用该模型对电池SOC进行预测,并将预测结果与其他模型预测结果相比较。结果显示,MOSFLA-FLN绝对误差不超过2.71,预测精度高,为SOC的精确计算提供了一种有效方法。
文摘Background： Geometric methods provide an analysis of autonomic modulation using the geometric properties of the resulting pattern, and represent an interesting tool in the analysis of heart rate variability （HRV）. The aim of this study was to evaluate the impact of functional training on cardiac autonomic modulation in healthy young women using the geometric indices of HRV. Methods： Data were analyzed from 29 women, and were stratified into a functional training group （FTG, n = 13; 23.00 ± 2.51 years; 21.90± 2.82 kg/m2） and a control group （CG, n = 16; 20.56 ± 1.03 years; 22.12±3.86 kg/m2）. The FTG received periodized functional training for 12 weeks. The cardiac autonomic modulation of both groups was evaluated before and after this training, and a qualitative analysis was performed using the Poincar6 plot. Results： There was a significant increase in the difference of the triangular index （RRTri）, SDI, SD2, and RR intervals in the FTG as compared to the CG, and the qualitative analysis from the Poincar6 plot showed an increase in the dispersion of beat-to-beat and long-term RR intervals in the functional group after training. No changes were observed in the triangular interpolation of RR interval histogram （TINN） or SD1/SD2. Conclusion： Functional training had a beneficial impact on autonomic modulation, as characterized by increased parasympathetic activity and overall variability, thus highlighting the clinical usefulness of this type of training.
