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.
Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.
Underwater acoustic signal is highly complex and difficult to predict. To improve the prediction accuracy of underwater acoustic signal, a complex underwater acoustic signal prediction method combining correlation variational mode decomposition (CVMD), least squares support vector machine (LSSVM) and Gaussian process regression (GPR) is proposed. Aiming at the problem of sample partitioning, this paper proposes a method of obtaining the embedding dimension and time delay based on the extreme learning machine prediction model. By selecting the appropriate time delay and embedding dimension, the prediction accuracy has improved. Aiming at the K-value selection of variational mode decomposition (VMD), this paper proposes a CVMD decomposition method, which improves the adaptability of VMD algorithm by selecting K-value through the correlation coefficient. Firstly, CVMD is used to decompose the underwater acoustic time series into several different components. Then, LSSVM prediction models are established for each component. Finally, to further improve the prediction accuracy of the model, Gaussian process regression (GPR) is used to correct the prediction result. One-step and multi-step prediction of underwater acoustic time series is carried out in this paper. Simulation results show that the model proposed in this paper has high prediction accuracy and can be effectively used in underwater acoustic signal prediction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.