TY -的A2 - G, Thippa Reddy AU -王,代表非盟-吴,城西盟——朱,云南盟——张明梁AU - Li Hanqiong盟——张魏PY - 2021 DA - 2021/10/07 TI -船舶辐射噪声识别技术基于ML-DS决策融合SP - 8901565六世- 2021 AB -船舶辐射噪声是一个重要的信息来源的水声目标,并对船舶识别和分类具有重要意义的目标。然而,有很多干扰噪音在水中,导致减少模型的识别率。因此,识别目标辐射噪声严重影响的结果。本文提出一种机器学习Dempster-Shafer (ML-DS)决策融合方法。机器学习的算法结合了识别结果和深度的学习。它使用循证决策理论来实现功能融合在不同的神经网络分类器,提高判断的准确性。首先,深入学习算法用于二维谱图特征进行分类和一维振幅特性提取CNN和LSTM网络。支持向量机的机器学习算法用于辐射噪声的色度特征进行分类。然后,根据不同的分类器的分类结果,基本概率分配模型(BPA)旨在融合分类器的识别结果。最后,根据机器学习的分类特征和深刻的学习,结合不同时期的d - s证据理论的决策,决策融合实现辐射噪声。 The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification. SN - 1687-5265 UR - https://doi.org/10.1155/2021/8901565 DO - 10.1155/2021/8901565 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -