Research on underwater confrontation prediction model based on BP neural network

Abstract

Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model, a prediction model of underwater countermeasures based on big data learning is designed. Firstly, the underwater countermeasure modeling of the underwater platform is carried out, and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method. At the same time, in order to solve the problem of poor time efficiency under massive simulation, BP neural network prediction algorithm is proposed for big data learning to provide accurate, fast and visual antagonistic results. The test results show that under the test environment set in this paper, the average prediction error of BP neural network prediction algorithm is 7.28% respectively, which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander’s command decision.

Keywords

• soft confrontation|big data|adversarial prediction model|avoidance probability