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

机译：基于果蝇优化算法和小波神经网络的混合模型及其在水下声信号预测中的应用

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摘要

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, thismodel 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.
机译：水下声信号的局部可预测性在水下声信号处理中起着重要作用，是非平稳信号检测的基础。小波神经网络模型，既具有小波分析又具有人工神经网络的优点，充分利用了小波分析的时频定位特性和人工神经网络的自学习能力。但是，该模型易于陷入局部最小值或产生收敛。为了克服这些缺点，提出了一种基于果蝇优化算法（FOA）和小波神经网络（WNN）的混合模型。通过优化小波神经网络的权重和阈值，构造了FOA-WNN预测模型，并将其应用于水下声信号预测。实验结果表明，与小波神经网络预测模型和BP神经网络预测模型相比，FOA-WNN预测模型具有较高的预测精度和较小的预测误差。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|3136267.1-3136267.8|共8页
  • 作者

    Yang Hong; Wang Siliang; Li Guohui; Mao Tongtong;

  • 作者单位

    Xian Univ Posts & Telecommun, Sch Elect Engn, Xian 710121, Shanxi, Peoples R China;

    Xian Univ Posts & Telecommun, Sch Elect Engn, Xian 710121, Shanxi, Peoples R China;

    Xian Univ Posts & Telecommun, Sch Elect Engn, Xian 710121, Shanxi, Peoples R China;

    Xian Univ Posts & Telecommun, Sch Elect Engn, Xian 710121, Shanxi, Peoples R China;

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