基于极限学习自编码器的水声信号目标识别方法
A Target Recognition Method for Underwater Acoustic Signals Based on Extreme Learning Autoencoder
曹琳1
作者信息
- 1. 水下测控技术重点实验室,辽宁 大连 116013
- 折叠
摘要
传统的机器学习方法在特征提取时容易受到主观经验的影响,导致对水声信号目标的识别准确率不高.而一般深度学习算法模型较复杂,通常具有训练耗时、计算复杂度高等缺点.极限学习自编码器具有很强的非线性处理能力,适合针对具有非线性特点的水声信号目标的识别,而且模型具有学习速度快,泛化能力强等显著优势.将极限学习自编码器算法应用于水声信号目标识别中,并与卷积神经网络、自编码器和极限学习机识别方法进行对比,结果表明:提出的方法对水声信号目标识别的准确率最优,且训练时间较短.
Abstract
Traditional machine learning methods are easily influenced by subjective experience during feature extraction,which leads to low recognition accuracy of underwater acoustic targets.However,deep learning algorithm models are relatively complex,which usually have the disadvantages of time-consuming training and high computational complexity.Extreme learning autoencoder has strong non-linear processing ability,which is suitable for recognition of underwater acoustic signals with nonlinear characteristics.Moreover,the model has significant advantages such as fast learning speed and strong generalization ability.In this paper,the extreme learning autoencoder algorithm is applied to underwater acoustic signal recognition,and is compared with convolutional neural networks,autoencoders,and extreme learning machine recognition methods.The results show that the proposed method has the best accuracy in target recognition of underwater acoustic signals and needs shorter training time.
关键词
水声信号目标识别/极限学习自编码器/卷积神经网络Key words
underwater acoustic signal target recognition/extreme learning autoencoder/convolutional neural network引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据