A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

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.

Underwater Acoustic Signal Prediction Based on Correlation Variational Mode Decomposition and Error Compensation

“…This algorithm optimizes the search by simulating the predatory behavior of whales, such as enveloping and bubble attacks. Compared with the classic fruit fly algorithm [8], and ant colony algorithm [36], WOA has the advantages of fewer parameters and strong optimization ability. For the specific steps of the WOA algorithm, please refer to [37], [38].…”

“…Sun et al [6] and Fang et al [7] used Volterra series theory to establish a nonlinear dynamic model of underwater acoustic signal, and realized the reduction of background noise and the suppression of reverberation interference through the local prediction of underwater acoustic signal. Yang et al [8] used the fruit fly optimization algorithm to optimize the wavelet neural network to improve the prediction accuracy of underwater acoustic signal. Although the single prediction model has achieved good results, the single prediction model often cannot achieve higher prediction accuracy.…”

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

“…He and Zhang et al [10] applied the BP neural network and RBF neural network to the prediction of underwater acoustic signal and achieved good prediction results. Yang et al [11] used the fruit fly algorithm to optimize the wavelet neural network, which further improved the prediction accuracy of underwater acoustic signal. Chitsazan et al [12] improved the classical echo state network and applied it to the prediction of wind speed and direction, with good prediction results.…”

A New Hybrid Model for Underwater Acoustic Signal Prediction

“…He et al [17] proposed the automatic search algorithm of particle swarm optimization (PSO) for RBF neural network based on the phase space theory to predict underwater acoustic signals. Yang et al [18] used wavelet neural network to predict underwater acoustic signal. Although 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.…”

“…As mentioned above, 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. Although several prediction methods have been developed, they still have some limitations: (i) it is found that a single prediction model cannot fully capture the nonlinear data information and for the requirements of high prediction accuracy; (ii) the traditional decomposition integration model uses the decomposition method to predict each mode component, which take a long computation time.…”