A New Hybrid Model for Underwater Acoustic Signal Prediction

Abstract

The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

“…Comparing the disadvantages of EEMD, variational mode decomposition (VMD) [ 41 ] has higher decomposition resolution and solved the mode aliasing problem. So VMD can better capture the inherent characteristics of data [ 42 , 43 ] and has been widely used in various time sequence prediction [ [44] , [45] , [46] , [47] ]. The research on the case prediction in COVID-19 by using the idea of decomposition-integration has just started.…”

A new hybrid prediction model of COVID-19 daily new case data

“…However, EEMD has two shortcomings such as lacking strict mathematical theoretical basis ( Li et al, 2018 ), and modal aliasing ( Jiang et al, 2019 , Lang et al, 2020 , Yang et al, 2021b ). Although VMD has a solid theoretical foundation and eliminates modal aliasing ( Dragomiretskiy and Zosso, 2014 , Li et al, 2020 , Yang et al, 2021a ), it relies too much on subjective setting of decomposition parameter and , which will lead to inaccurate decomposition results. Summary of these methods mentioned in introduction is shown in Table 1 .…”

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“…Compared with EEMD, CEEMD and CEEMDAN, variational mode decomposition (VMD) not only has a solid theoretical basis, but also overcomes the phenomenon of modal aliasing ( Dragomiretskiy and Zosso, 2014 , Li et al, 2020 , Yang et al, 2021 ) and can better capture the inherent characteristics of data. However, this predictive thinking is just beginning to be applied to the COVID-19 confirmed data prediction.…”