Conclusion UPOs can be used to characterize the dynamics of HRV and is a potential method to analyze HRV.
结论非稳定周期轨道可以刻划hrv的动力学性质，是分析HRV的潜在的方法。
The advantage of this method is that it does not need to know any information about the UPOs embeded in spatiotemporal chaotic system.
这一控制方法的优点是不需要获取时空混沌系统中不稳定周期轨道的任何信息，且控制参数与被控系统的参数和变量的取值无关。
The results show that the UPOs embedded in the chaotic system can be stably controlled by changing the linear transformation matrix of system variables.
结果表明，通过改变系统变量之间的线性变换矩阵，可以实现混沌系统中各种不稳定周期轨道的稳定控制。
Finally, data validation and optimization value finding software is made and integrated into UPOS according to the theory of data mining and knowledge engineering.
最后，论文基于数据挖掘的理论和知识工程的增量式开发方法研制了运行优化数据检验软件和优化目标值挖掘软件。
The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of "unstable periodic orbits" (UPOs).
这种分析的概念的基础是将所观察到的神经元活动抽象成用非稳定周期轨道的分级所描述的动力学图。
Objective To study structure of the unstable periodic orbits (UPOs) of R-R interval signals of heart during orthostatic standing, and to reveal the dynamic characters of heart rate variability (HRV).
目的研究立位心脏R- R间期信号的非稳定周期轨道的结构，进一步探讨心率变异（HRV）的动力学特征。
Objective To study structure of the unstable periodic orbits (UPOs) of R-R interval signals of heart during orthostatic standing, and to reveal the dynamic characters of heart rate variability (HRV).
目的研究立位心脏R- R间期信号的非稳定周期轨道的结构，进一步探讨心率变异（HRV）的动力学特征。
应用推荐