Effects of Correlated Noise on the Performance of Persistence Based Dynamic State Detection Methods
Effects of Correlated Noise on the Performance of Persistence Based Dynamic State Detection Methods
复制标题
相关噪声对基于持久性的动态状态检测方法性能的影响
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Firas A. Khasawneh
中科院分区：
文献类型：
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作者：
J. Tempelman;Audun D. Myers;J. Scruggs;Firas A. Khasawneh
描述动态系统状态的能力一直是时间序列分析界的一项重要任务。传统的措施，如李雅普诺夫指数往往难以恢复噪声数据，特别是如果系统的维数是未知的。最近的基于二进制和网络的测试方法已经为未知的确定性系统提供了有希望的结果，但是注入到周期信号中的噪声会导致误报。最近，我们展示了使用持久同源性作为工具来实现未知模型系统的动态状态检测的优势，并展示了其对白色高斯噪声的鲁棒性。在这项工作中，我们探讨了基于持久性的方法对色噪声影响的鲁棒性，并表明1/ f α形式的色噪声过程在α < 0的较低信噪比下导致假阳性诊断。
The ability to characterize the state of dynamic systems has been a pertinent task in the time series analysis community. Traditional measures such as Lyapunov exponents are often times difficult to recover from noisy data, especially if the dimensionality of the system is not known. More recent binary and network based testing methods have delivered promising results for unknown deterministic systems, however noise injected into a periodic signal leads to false positives. Recently, we showed the advantage of using persistent homology as a tool for achieving dynamic state detection for systems with no known model and showed its robustness to white Gaussian noise. In this work, we explore the robustness of the persistence based methods to the influence of colored noise and show that colored noise processes of the form 1/ f α lead to false positive diagnostic at lower signal to noise ratios for α < 0.
影响因子：
2.1
作者：
Gottwald, Georg A.;Melbourne, Ian
通讯作者：
Melbourne, Ian