CRH(China Railway High Speed)动车组高速列车已成为我国最重要的交通工具,而高速列车牵引电机的可靠性对于保障列车安全运行具有重要意义。提出一种基于改进双曲交点算法的参数估计方法,采用双曲交点作为搜索点,通过约束条件限制搜索...CRH(China Railway High Speed)动车组高速列车已成为我国最重要的交通工具,而高速列车牵引电机的可靠性对于保障列车安全运行具有重要意义。提出一种基于改进双曲交点算法的参数估计方法,采用双曲交点作为搜索点,通过约束条件限制搜索点的数目,并在参数估计过程中改变控制参数调节算法的自适应性,以提高参数估计的效率和准确率。以CRH2高速列车牵引电机为模型,基于数学模型在Matlab/Simulink中建立仿真模型,结合所提出的算法进行参数估计。研究结果表明,提出的参数估计方法,能够有效地提高电机故障诊断效率并准确诊断电机定子绕组故障,验证了所提算法的有效性。展开更多
The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an app...The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.展开更多
文摘CRH(China Railway High Speed)动车组高速列车已成为我国最重要的交通工具,而高速列车牵引电机的可靠性对于保障列车安全运行具有重要意义。提出一种基于改进双曲交点算法的参数估计方法,采用双曲交点作为搜索点,通过约束条件限制搜索点的数目,并在参数估计过程中改变控制参数调节算法的自适应性,以提高参数估计的效率和准确率。以CRH2高速列车牵引电机为模型,基于数学模型在Matlab/Simulink中建立仿真模型,结合所提出的算法进行参数估计。研究结果表明,提出的参数估计方法,能够有效地提高电机故障诊断效率并准确诊断电机定子绕组故障,验证了所提算法的有效性。
基金supported by National Natural Science Foundation of China(Grant Nos.11001124 and 91130007)the Doctoral Fund of Ministry of Eduction of China(Grant No.20110091110004)the General Research Fund from Hong Kong Research Grants Council(Grant No.HKBU 203712)
文摘The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.
