IIR implementation of wavelet decomposition for digital signal analysis

1992 ◽  
Vol 28 (5) ◽  
pp. 513 ◽  
Author(s):  
F. Argenti ◽  
G. Benelli ◽  
A. Sciorpes
2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang

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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158961-158971
Author(s):  
Hui Guo ◽  
Shuguang Huang ◽  
Cheng Huang ◽  
Zulie Pan ◽  
Min Zhang ◽  
...  

1976 ◽  
Vol SMC-6 (10) ◽  
pp. 724-724 ◽  
Author(s):  
S. D. Stearns ◽  
N. Ahmed

IEEE Spectrum ◽  
1971 ◽  
Vol 8 (4) ◽  
pp. 62-70 ◽  
Author(s):  
Peter R. Roth

2017 ◽  
Vol 22 (S5) ◽  
pp. 11129-11141 ◽  
Author(s):  
Huan Wang ◽  
Min Ouyang ◽  
Zhibing Wang ◽  
Ruishi Liang ◽  
Xin Zhou

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