Abstract Variational mode decomposition (VMD) is a powerful technique for concurrently decomposing a signal into its constituent intrinsic modes. However, the performance of VMD will be degraded if the number of modes available in the signal is not precisely known. In this paper, we introduce a new method, namely successive variational mode decomposition (SVMD), which extracts the modes successively and does not need to know the number of modes. The method considers the mode as a signal with maximally compact spectrum, as VMD does. It achieves the mode decomposition by adding some criteria to the optimization problem of VMD: the mode of interest has no or less spectral overlap to the other modes and to the residual signal. Our simulations on some artificial and real world data have demonstrated that the new method without knowing the number of modes converges to the same modes as VMD does with knowing the precise number of modes. Moreover, the computational complexity of SVMD is much lower than that of VMD. Another advantage of SVMD over VMD is more robustness against the initial values of the center frequencies of modes.

中文翻译：

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连续变分模式分解

摘要 变分模式分解 (VMD) 是一种强大的技术，可将信号同时分解为其组成的固有模式。但是，如果信号中可用的模式数量不准确，VMD 的性能将会下降。在本文中，我们介绍了一种新的方法，即连续变分模式分解（SVMD），它连续提取模式并且不需要知道模式的数量。该方法将模式视为具有最大紧凑频谱的信号，就像 VMD 一样。它通过向 VMD 的优化问题添加一些标准来实现模式分解：感兴趣的模式与其他模式和残差信号没有或很少有频谱重叠。我们对一些人工和真实世界数据的模拟表明，不知道模式数量的新方法收敛到与 VMD 相同的模式，因为知道确切的模式数量。而且，SVMD的计算复杂度远低于VMD。与 VMD 相比，SVMD 的另一个优点是对模式中心频率的初始值具有更强的鲁棒性。