状态空间
噪音(视频)
希尔伯特-黄变换
模式(计算机接口)
计算机科学
代表(政治)
状态空间表示
非线性系统
空格(标点符号)
国家(计算机科学)
实证研究
数据挖掘
算法
数学
人工智能
白噪声
统计
物理
量子力学
电信
政治
操作系统
图像(数学)
法学
政治学
作者
Joseph Park,Gerald M. Pao,George Sugihara,Erik Stabenau,Thomas Lorimer
标识
DOI:10.1007/s11071-022-07311-y
摘要
Abstract Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems. Methods that operate in the system state-space require either an explicit multidimensional state-space, or, one approximated from available observations. Since observational data are frequently sampled with noise, it is possible that noise can corrupt the state-space representation degrading analytical performance. Here, we evaluate the synthesis of empirical mode decomposition with empirical dynamic modeling, which we term empirical mode modeling, to increase the information content of state-space representations in the presence of noise. Evaluation of a mathematical, and, an ecologically important geophysical application across three different state-space representations suggests that empirical mode modeling may be a useful technique for data-driven, model-free, state-space analysis in the presence of noise.
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