现代防御技术2018，Vol.46Issue(2)：122-129,8.DOI:10.3969/j.issn.1009-086x.2018.02.020
无监督神经网络的潜艇对空战术意图识别
Unsupervised Learning Neural Network Based Submarine Recognize Tactical Intention for Air Target
张天赫 1彭绍雄 1邹强 1王栋1
作者信息
- 1. 海军航空工程学院,山东 烟台 264001
- 折叠
摘要
Abstract
The traditional way to obtain the air target information through the remote sensing system so as to resolve the target tactical intent requires a large number of experts to evaluate the network nodes and weights with a slow speed,high cost and other shortcomings.To reduce the recognition time of air combat,the computing ability of unsupervised learning neural network is brought into full play.The air target attribute and target tactical intent acquired from remote sensing are used to form the training samples to train the neural network,and thus the input threshold target attribute and relationship between neurons in competitive layer are acquired.The output function is established,and the air target tactical intention is identified.The simulation results show that the output value of the test sample trained by the competitive neural network and self-organizing feature maping (SOFM) neural network corresponds to the real value,and the accuracy is higher.关键词
神经网络/意图识别/神经元/竞争层/潜艇/战术意图Key words
neural network/intention recognition/neuron/competitive layer/submarine/tactical intention分类
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张天赫,彭绍雄,邹强,王栋..无监督神经网络的潜艇对空战术意图识别[J].现代防御技术,2018,46(2):122-129,8.
