A New Hybrid Model for Underwater Acoustic Signal Prediction

Guohui Li,Wanni Chang,Hong Yang

doi:10.1155/2020/5167469

Guohui Li, Wanni Chang + Show 1 more

Open Access

https://doi.org/10.1155/2020/5167469

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Journal: Complexity	Publication Date: Jul 25, 2020
Citations: 6	License type: CC BY 4.0

Affiliation: Xi’an University of Posts and Telecommunications

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.

Highlights

Underwater acoustic signal processing is one of the most active disciplines in the field of ocean and information [1]
In order to further measure the prediction effect of this method, the variational mode decomposition (VMD)-dispersion entropy (DE)-extreme learning machine (ELM)-ASVR model proposed in this paper is compared with the other six models, and the prediction effect of each model is quantitatively analyzed by mean absolute error (MAE), root mean squared error (RMSE), and R2, so as to verify the superiority of the combined model in the prediction performance of this paper
In order to improve the prediction accuracy of underwater acoustic signal, a combined prediction model based on VMD-DE-ELM-ASVR is proposed and applied to the prediction of underwater acoustic signal. e main conclusions are as follows: (1) e VMD decomposition algorithm can effectively overcome the mode mixing of Empirical mode decomposition (EMD). e simulation results show that the decomposition effect of VMD is clearer, and the prediction accuracy is higher

Summary

Introduction

Underwater acoustic signal processing is one of the most active disciplines in the field of ocean and information [1]. Artificial neural network and Volterra nonlinear model are mainly used to predict underwater acoustic signal of ships. The above methods have achieved good prediction results of underwater acoustic signal, the neural network is easy to fall into local optimum, long calculation time, and easy to oscillate. It maps the original data to high-dimensional space by nonlinear function to expand regression analysis It has obvious advantages in solving nonlinear problems and can effectively improve the generalization ability and prediction accuracy of the model [22,23,24]. Some prediction methods of underwater acoustic signal such as Volterra model [13], wavelet neural network [18], and mode decomposition technology combined prediction method [21, 43] were proposed for different prediction models. E main contents of this paper are as follows: in Section 2, the basic theory for each part of the hybrid prediction model will be introduced; in Section 3, the overall framework of the model will be presented; in Section 4, results and discussion for the proposed prediction hybrid model for underwater acoustic signal will be discussed; and conclusions will be presented as the last section of this paper

Basic Theory

ABC-SVR

The Prediction Model for Underwater Acoustic Signal

Results and Discussion

Conclusions