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A Perturbative-Based Generalized Series Expansion in Terms of Non-Orthogonal Component Functions

机译:基于非正交分量函数的基于摄动的广义级数展开

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In this paper we present a generalized perturbative approximate series expansion in terms of non-orthogonal component functions. The expansion is based on a perturbative formulation where, in the non-orthogonal case, the contribution of a given component function, at each point, in the time domain or frequency in the Fourier domain, is assumed to be perturbed by contributions from the other component functions in the set. In the case of orthogonal basis functions, the formulation reduces to the non-perturbative case approximate series expansion. Application of the series expansion is demonstrated in the context of two non-orthogonal component function sets. The technique is applied to a series of non-orthogonalized Bessel functions of the first kind that are used to construct a compound function for which the coefficients are determined utilizing the proposed approach. In a second application, the technique is applied to an example associated with the inverse problem in electrophysiology and is demonstrated through decomposition of a compound evoked potential from a peripheral nerve trunk in terms of contributing evoked potentials from individual nerve fibers of varying diameter. An additional application of the perturbative approximation is illustrated in the context of a trigonometric Fourier series representation of a continuous time signal where the technique is used to compute an approximation of the Fourier series coefficients. From these examples, it will be demonstrated that in the case of non-orthogonal component functions, the technique performs significantly better than the generalized Fourier series which can yield nonsensical results.
机译:在本文中,我们提出了一种基于非正交分量函数的广义摄动近似级数展开。展开基于微扰公式,在非正交情况下,假定给定分量函数在傅立叶域的时域或频率上的每个点的贡献都被另一个分量的摄动所扰动。组件中的功能。在正交基函数的情况下,公式可简化为非扰动情况下的近似级数展开。在两个非正交分量函数集的上下文中证明了级数展开的应用。将该技术应用于一系列第一类非正交贝塞尔函数,这些函数用于构造复合函数,利用建议的方法为其确定系数。在第二个应用中,该技术被应用于与电生理学中的逆问题相关的示例,并且通过从周围神经干中分解出复合诱发电位来证明,该复合诱发电位来自不同直径的单个神经纤维的诱发电位。在连续时间信号的三角傅里叶级数表示的背景下,说明了摄动近似的另一种应用,其中该技术用于计算傅里叶级数系数​​的近似值。从这些示例中,将证明,在非正交分量函数的情况下,该技术的性能明显优于广义傅立叶级数,后者可以产生无意义的结果。

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