a hybrid model based on wavelet decomposition-reconstruction in track irregularity state forecasting
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ID: 238474
2015
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Abstract
Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.
| Reference Key |
jia2015mathematicala
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|---|---|
| Authors | ;Chaolong Jia;Lili Wei;Hanning Wang;Jiulin Yang |
| Journal | journal of power sources |
| Year | 2015 |
| DOI |
10.1155/2015/548720
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| URL | |
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