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On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data

机译:关于Holo-Hilbert谱分析:非线性和非平稳数据的完整信息谱表示

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摘要

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.
机译:引入Holo-Hilbert频谱分析(HHSA)方法可解决传统频谱分析的不足,并提供非线性和非平稳数据的完整信息表示。它使用嵌套的经验模式分解和Hilbert-Huang变换(HHT)方法来识别非线性系统中经常出现的固有振幅和频率调制。首先是与传统频谱分析进行比较,传统频谱分析通常通过基于先验确定基础的加性展开的卷积积分变换来获得结果,通常是在线性和固定假设下进行的。因此,对于非平稳过程,历史上最好的方法是使用时频表示,其中幅度(或能量密度)的变化仍以时间表示。对于非线性过程,数据可以具有通过两种不同的机制生成的幅度调制和频率调制(模内和模间):线性加法或非线性乘法过程。由于所有现有的光谱分析方法都基于先验或自适应的加法展开,因此它们都不可能代表乘法过程。尽管较早的自适应HHT频谱分析方法可以非常显着地适应波内非线性,但仍然没有处理任何包含跨尺度耦合和锁相调制的波间非线性乘法机制。为了解决乘法过程问题,需要在频谱结果中附加维数以同时解决振幅和频率调制方面的变化。 HHSA适应所有过程:加法和乘法,模内和模间,平稳和非平稳,线性和非线性相互作用。 HHSA中的Holo前缀表示具有加法和乘法功能的多维表示。

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