不同初始平衡磁剪切对共振磁扰动诱发的双撕裂模的影响
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摘要
为了量化分析不同初始平衡磁剪切对共振磁扰动诱发的双撕裂模的影响,提出基于非线性多参数约束的不同初始平衡磁剪切对共振磁扰动诱发的双撕裂模控制模型。构建磁剪切对共振磁扰动诱发的控制约束参量模型,采用极低频外电场持续扰动方法进行不同初始平衡磁剪切对共振磁扰动建模,结合容积卡尔曼滤波方法进行共振磁扰动诱发的抑制处理,提取不同初始平衡磁剪切对共振磁扰动诱发的量化特征量。采用随机量化博弈和定量递归分析方法进行共振磁扰动诱发的双撕裂模自适应控制,分析共振磁扰动诱发对双撕裂模的稳定性控制因素,结合不同的刺激参数进行自适应参数估计和寻优,实现不同初始平衡磁剪切对共振磁扰动诱发的双撕裂模的定量递归分析。仿真结果表明,采用该方法进行共振磁扰动诱发双撕裂模的稳定性控制能力较好,收敛性较强,提高了双撕裂模的磁鲁棒性。
In order to quantitatively analyze the effect of different initial equilibrium magnetic shear on the double tearing mode induced by resonance magnetic disturbance,a double tearing mode control model with different initial equilibrium magnetic shear induced by resonant magnetic disturbance based on nonlinear multi-parameter constraints is proposed. The control constraint parameter model induced by magnetic shear induced by resonance magnetic disturbance is constructed,and the resonant magnetic disturbance model with different initial equilibrium magnetic shear is established by using the method of very low frequency external electric field continuous disturbance. The volume Kalman filtering method is used to suppress the magnetic disturbance induced by resonance magnetic disturbance, and the quantitative characteristics of different initial equilibrium magnetic shear induced by the resonance magnetic disturbance are extracted. The adaptive control of double tearing mode induced by resonance magnetic disturbance is carried out by means of stochastic quantitative game and quantitative recursive analysis,and the factors controlling the stability of double tearing mode induced by resonance magneticdisturbance are analyzed. Combined with different stimulus parameters,adaptive parameter estimation and optimization are carried out,and quantitative recursive analysis of double tearing modes induced by resonance magnetic disturbance with different initial equilibrium magnetic shear is realized. The simulation results show that the proposed method has good stability control ability and strong convergence and improves the magnetic robustness of the double tearing mode induced by resonance magnetic disturbance.
In order to quantitatively analyze the effect of different initial equilibrium magnetic shear on the double tearing mode induced by resonance magnetic disturbance,a double tearing mode control model with different initial equilibrium magnetic shear induced by resonant magnetic disturbance based on nonlinear multi-parameter constraints is proposed. The control constraint parameter model induced by magnetic shear induced by resonance magnetic disturbance is constructed,and the resonant magnetic disturbance model with different initial equilibrium magnetic shear is established by using the method of very low frequency external electric field continuous disturbance. The volume Kalman filtering method is used to suppress the magnetic disturbance induced by resonance magnetic disturbance, and the quantitative characteristics of different initial equilibrium magnetic shear induced by the resonance magnetic disturbance are extracted. The adaptive control of double tearing mode induced by resonance magnetic disturbance is carried out by means of stochastic quantitative game and quantitative recursive analysis,and the factors controlling the stability of double tearing mode induced by resonance magneticdisturbance are analyzed. Combined with different stimulus parameters,adaptive parameter estimation and optimization are carried out,and quantitative recursive analysis of double tearing modes induced by resonance magnetic disturbance with different initial equilibrium magnetic shear is realized. The simulation results show that the proposed method has good stability control ability and strong convergence and improves the magnetic robustness of the double tearing mode induced by resonance magnetic disturbance.
引文
[1]张俊锋,苏健军,姬建荣,等.热容式热流密度传感器的研制及应用[J].中国测试,2018,44(10):125-129.
[2]徐乃凡,王俊芳,郭建立,等.面向边缘云高效能的移动终端计算迁移方法[J].电子测量技术,2018,41(20):1-6.
[3]戴洪德,李娟,景博,等.基于HP滤波与非线性维纳过程的退化建模与失效分布研究[J].仪器仪表学报,2018,39(08):63-71.
[4] SHEN Ying,HUANG Zhangcan,TAN Qing,et al. Krill herd algorithm based on dynamic pressure control operator[J]. Journal of Computer Applications,2019,39(3):663-667.
[5] HAN Jianghong,YUAN Jiaxuan,WEI Xing,et al.Pedestrian visual positioning algorithm for underground roadway based on deep learning[J]. Journal of Computer Applications,2019,39(3):688-694.
[6] LI Qiuzhen, LUAN Chaoyang, WANG Shuangxi.Quality evaluation of face image based on convolutional neural network[J]. Journal of Computer Applications,2019,39(3):695-699.
[7]杨季三,徐贵力,董文德,等.微波叶尖间隙传感器信号校准研究[J].仪器仪表学报,2018,39(10):193-201.
[8]张笑笑,储昭碧,董学平.基于滑动平均的并联Buck变换器均流控制[J].电子测量与仪器学报,2018,32(10):177-182.
[9] FARNADI G,BACH S H,MOENS M F,et al. Soft quantification in statistical relational learning[J].Machine Learning,2017,106(12):1971-1991.
[10] RAMES A, RODRIGUEZ M, GETOOR L. Multirelational influence models for online professional networks[C]//Proceedings of the 2017 International Conference on Web Intelligence. New York:ACM,2017:291-298.
[2]徐乃凡,王俊芳,郭建立,等.面向边缘云高效能的移动终端计算迁移方法[J].电子测量技术,2018,41(20):1-6.
[3]戴洪德,李娟,景博,等.基于HP滤波与非线性维纳过程的退化建模与失效分布研究[J].仪器仪表学报,2018,39(08):63-71.
[4] SHEN Ying,HUANG Zhangcan,TAN Qing,et al. Krill herd algorithm based on dynamic pressure control operator[J]. Journal of Computer Applications,2019,39(3):663-667.
[5] HAN Jianghong,YUAN Jiaxuan,WEI Xing,et al.Pedestrian visual positioning algorithm for underground roadway based on deep learning[J]. Journal of Computer Applications,2019,39(3):688-694.
[6] LI Qiuzhen, LUAN Chaoyang, WANG Shuangxi.Quality evaluation of face image based on convolutional neural network[J]. Journal of Computer Applications,2019,39(3):695-699.
[7]杨季三,徐贵力,董文德,等.微波叶尖间隙传感器信号校准研究[J].仪器仪表学报,2018,39(10):193-201.
[8]张笑笑,储昭碧,董学平.基于滑动平均的并联Buck变换器均流控制[J].电子测量与仪器学报,2018,32(10):177-182.
[9] FARNADI G,BACH S H,MOENS M F,et al. Soft quantification in statistical relational learning[J].Machine Learning,2017,106(12):1971-1991.
[10] RAMES A, RODRIGUEZ M, GETOOR L. Multirelational influence models for online professional networks[C]//Proceedings of the 2017 International Conference on Web Intelligence. New York:ACM,2017:291-298.