摘要
在现代战争中,各种水声探测设备被开发出来,用于对我方船只和敌对船只的监测。这些水声设备的基本原理都类似,即基于对水压场的探测,获得目标物的信号特征,并由此甄别出目标物的运动状态。如今人工智能的应用,极大提高了水压场信号的检测水平。本文重点研究舰船水压场的信号检测技术,并基于神经网络算法对舰船的水压场模型进行分析。通过对水压场信号的分析,获得舰船模型的水压场数据。利用Autodyn软件对模拟船舶的水压场状态进行仿真,给出压力强度变化曲线。
In modern war,all kinds of underwater acoustic detection devices have been developed,for our hostile ships and vessels monitored and the basic principles of these devices are similar to the sound of water,which is based on the detection of pressure field obtained it was the signal characteristics of the target,and thereby identify the moving object 's state. Today,application of artificial intelligence,has greatly increased the level of detection hydraulic pressure signal. This paper focuses on the technical ship hydraulic pressure signal detection,and the neural network algorithm based on a model ship hydraulic pressure field analysis. By hydraulic pressure signal analysis obtained pressure field data model ship. AUTODYN use software to simulate a ship of state pressure field simulation,and given the strength of the pressure curve.
出处
《舰船科学技术》
北大核心
2016年第9X期46-48,共3页
Ship Science and Technology
关键词
神经网络
水压场
信号检测
neural network
water pressure field
single detection
