Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variat...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-17
Main Authors: Yang, Hong, Li, Guohui, Gao, Lipeng
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Algorithms
Analysis
Artificial neural networks
Blindness
Decomposition
Forecasts and trends
Fourier transforms
Kernels
Machine learning
Mathematical models
Methods
Model accuracy
Neural networks
Noise
Optimization
Parameters
Prediction models
Random variables
Regularization
Short term
Signal processing
Time series
Underwater acoustics
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Summary:Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/6947059