摘要
运用径向基神经网络,利用水下振动物体内表面加速度信号对其辐射噪声级别进行分类,达到判断其声隐身性的目的.该方法的运算量较传统方法大大降低,极大地提高了计算速度.实例表明,该方法能较准确地对水下振动物体辐射声场声压级别进行分类,进而对其推广应用于潜艇提供了较好的依据.
By the use of RBF neural network, acceleration signals on the inside surface of the underwater vibrating object are used to classify its sound radiation level in order to measure its acoustic stealth. The method has much less complexity of computation than the traditional method, and the computing speed is improved a lot. The example shows that this method has good ability of classifying the underwater vibrating object sound radiation level, and also provides a nice basis on the possibility to use this method on submarine.
出处
《海军工程大学学报》
CAS
北大核心
2005年第4期93-96,共4页
Journal of Naval University of Engineering
关键词
辐射噪声
分级报警
RBF神经网络
高斯核函数
sound radiation
classifying alarm
RBF neural network
Gaussian kernel function
