Spectrum data feature analysis based on support vector machine method

Wu, Jiayi; Cui, Shuo; Su, Donglin

doi:10.1109/apcap.2017.8420531

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…27 (d). There are three maximal order unions (1,20), (5,20), and (8,20) of 0.08% corresponding to the first, fifth, eighth, and 20 th marked point frequencies from left to right in Fig. 26 (d).…”

Section: Order Reduced Processing For Multi-sourcementioning

confidence: 98%

“…In recent years, the artificial neural networks gain much attention and are used to find interference sources for nonlinear systems [6]. The Support Vector Machines (SVMs) are used to identify electromagnetic radiation sources for complex electronic systems [7], [8]. In addition, a fault diagnosis approach incorporated with the SVMs is proposed to analyze nonlinear spectrum [9].…”

Section: Introductionmentioning

confidence: 99%

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A New Electromagnetic Emission Source Frequency and Factor Identification Approach for Nonlinear Circuits From the Output Spectrum Data

Yang

et al. 2020

IEEE Access

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For the practical nonlinear circuits, a number of new frequencies are generated in the output spectrum, which makes electromagnetic interference location quite challenging. To mitigate this problem, an approach is proposed to identify the electromagnetic emission source frequencies and nonlinear factors from the output spectrum of nonlinear circuits. The amplitude of the output spectrum with the input of two sine signals is first calculated based on the power series. Then, the Order Image (OI) is defined to reveal the output spectrum features, and its symmetry characteristic has been comprehensively analyzed. Results show that the maximum symmetry index is observed when the reference frequencies are equal to the excitation frequencies. Based on this observation, the electromagnetic emission source frequencies can be obtained. In addition, a Backward Algorithm (BA) has been introduced to extract nonlinear factors. The proposed approach only requires the output spectrum and the amplitude of one excitation. Compared with other techniques, like the Support Vector Machines and the Recurrent Neural Network, it needs much less prior information. Finally, several examples are carried out to verify the effectiveness of the proposed approach. Results show that the emission source frequencies and nonlinear factors can be accurately extracted. Therefore, the proposed approach shows great potential in electromagnetic interference location and characteristic analysis for nonlinear circuits. INDEX TERMS Electromagnetic emission sources, identification, nonlinear circuits, Order Image, power series.

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