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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 대한전자공학회 대한전자공학회 학술대회 ICEIC 2017 International Conference on Electronics, Information, and Communication
- 발행연도
- 2017.1
- 수록면
- 334 - 339 (6page)
이용수
초록· 키워드
The conventional analysis of temporal signals rely on techniques such as Fourier transform, wavelet analysis, Wigner- Vile distribution, and nonlinear time series analysis methods, that assume either stationarity, linearity, or both. On the other hand, the empirical mode decomposition (EMD) is an adaptive algorithm that decomposes a signal into its fundamental modes of oscillations, or intrinsic mode functions (IMF), and has been shown to produce a meaningful representation of nonlinear and nonstationary processes commonly found in fields such as physics and biology. One of the main problems of the EMD algorithm is the occurrence of end effects, i.e, if the endpoint of the signal is not the extreme point, as the algorithm is applied there appear end swings which back propagates, leading to distorted components in the estimated IMF. Here we introduce a modification to the EMD algorithm to constraint the end effect, based on the use of a Nash nonlinear grey Bernoulli model (NNGBM) to forecast the signal’s boundary. Numerical simulations show that our approach estimates all IMFs with a higher degree of accuracy than previous methods and is more stable under increases in the nonlinear and non-stationarity properties of the signals.
#empirical mode decomposition
#Hilbert-Huang transform
#grey model
#Nash nonlinear grey Bernoulli model
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목차
- Abstract
- I. INTRODUCTION
- II. THEORY
- III. RESULTS
- IV. DISCUSSION AND CONCLUSIONS
- REFERENCES
참고문헌
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UCI(KEPA) : I410-ECN-0101-2017-569-002194833
