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

Hong Yang,Lipeng Gao,Guohui Li

doi:10.1155/2020/6947059

Abstract

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.

Highlights

Underwater acoustic signal prediction is the basis of underwater acoustic signal processing, which can be applied to many aspects such as underwater target signal reduction, detection, and feature extraction [1,2,3]. e underwater acoustic signal has nonlinear, non-Gaussian, and nonstationary characteristics and typical chaotic, fractal, and other characteristics [4, 5], which provide a basis for short-term prediction of underwater acoustic signal.In recent years, with continuous development of ship noise reduction technology, the detection of underwater target signal is more and more difficult
In order to overcome the influence of K value selection on the decomposition effect, this paper proposes a method of selecting the number of modes based on mutual information, called the MVMD algorithm
In order to verify the validity of the MVMD-optimized kernel extreme learning machine (OKELM) prediction model, the MVMD-OKELM prediction model is applied to chaotic time series prediction and underwater acoustic signal time series prediction, respectively

Summary

Introduction

Underwater acoustic signal prediction is the basis of underwater acoustic signal processing, which can be applied to many aspects such as underwater target signal reduction, detection, and feature extraction [1,2,3]. e underwater acoustic signal has nonlinear, non-Gaussian, and nonstationary characteristics and typical chaotic, fractal, and other characteristics [4, 5], which provide a basis for short-term prediction of underwater acoustic signal. Buyuksahin and Ertekin [15] proposed a hybrid prediction method combining ARIMA and artificial neural network (ANN) and added empirical mode decomposition technology to further improve the prediction accuracy of time series. The combination of decomposition methods and various prediction models can effectively improve the prediction accuracy of time series This kind of report on the prediction of underwater acoustic signal using decomposition prediction idea is rare, so it is necessary to carry out further research in this field. If an appropriate decomposition method can be selected, the original complex signal can be decomposed into multiple stationary component signals, so as to optimize the prediction model and improve the prediction accuracy. To fully illustrate the effectiveness of MVMD-OKELM in time series prediction, this paper applies the proposed MVMDOKELM prediction model to Mackey–Glass chaotic time series and underwater acoustic signal, respectively, and compares it with ARIMA, EMD-OKELM, and other prediction models

Basic Theory

Application Examples

Findings

Conclusions