Unsupervised Learning Neural Network Based Submarine Recognize Tactical Intention for Air Target

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.