Abstract
The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.
Highlights
An important feature of the underwater acoustic signal is local predictability
The fly optimization algorithm (FOA)-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction
The RMS error (RMSE) and mean absolute error (MAE) of the FOAWNN prediction model are 0.0498 and 0.0387, respectively; those of the WNN prediction model are 0.0573 and 0.0468, respectively; and those of the BP neural network prediction model are 0.0701 and 0.0534, respectively. These results show that the fly optimization algorithm and wavelet neural network (FOA-WNN) has the highest accuracy
Summary
An important feature of the underwater acoustic signal is local predictability. This feature plays an important role in underwater acoustic signal processing and is the basis for solving nonstationary signal detection [1]. In [4,5,6,7], radial basis function neural network (RBF) is used to establish the prediction model of underwater acoustic signal. In [15, 16], FOA is used to automatically determine optimal parameters of the least square support vector machine model and complete the prediction of random terms and periodic terms This algorithm is used for analysis of calculated and measured data of both “acoustic Goos-Hanchen effect” induced at liquid-solid interface [17] and polarization state of Mathematical Problems in Engineering inhomogeneous mode conversion wave created at anisotropic rock interface [18]. A new hybrid model based on fruit fly optimization algorithm and wavelet neural network (FOA-WNN) is proposed and applied to underwater acoustic signal prediction
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