Study on the Aerial Target's Threat Degree Ordering Model Based on BP Neural Network

摘要

This paper introduces the significance of air-raiding and anti-air-raiding for modern war and ordering of air attack targets to air defense, analyzes the influence index of aerial target's threat degree, and determines the normalized treatment of these influence indexes. The paper brings in BP neural network theory and illustrates its algorithm steps and establishes the model of BP neural network, training using the constructed network to sort the threat degree of aerial targets. Result shows that the established BP neural network model has a strong nonlinear mapping ability and self-learning ability, it provides a reference for the air defense unit to evaluate aerial target's threat degree, so could help commanders to make better decisions.