Method for prejudging intention of warship to attack air target

Abstract

[Objectives] This paper proposes a heterogeneous integrated learner to solve the problem of fuzzy uncertainty classification in order to judge the target intention of air attack in a short time. [Methods] First, a limit learning machine, decision tree, Skohonen neural network and LVQ neural network are selected to construct the heterogeneous integrated learner using the integrated learning strategy. Next, the training program is trained 100 times using the integrated learner to obtain the classification experiment average accuracy and calculation time. In order to improve the accuracy, integrated pruning is carried out to eliminate the "poor quality" LVQ neural network, and a more efficient heterogeneous integrated learner is reconstructed. The experimental results are extremely accurate but the calculation is time-consuming. In this paper, the Skohonen neural network sub-classifier is proposed as an "offline training and online call". [Results] Simulation experiments show that the time consumed from detecting the air targets to prejudging the intention of each incoming target is 4.972 s with an accuracy of 99.93%, which is excellent for meeting accuracy and real-time requirements. [Conclusions] This study provides a new and effective method for air defense decision-making. The method used in this paper also provides a better way of realizing the classification problem of small samples.

Keywords

• integrated learning
• extreme learning machine
• decision tree
• Skohonen neural network
• LVQ neural network
• integrated pruning