An approach for predicting digital material consumption in electronic warfare

Abstract

Electronic warfare is a modern combat mode, in which predicting digital material consumption is a key for material requirements planning (MRP). In this paper, we introduce an insensitive loss function (ε) and propose a ε-SVR-based prediction approach. First, we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set, and preprocess it to construct the sample space. Subsequently, we establish the ε-SVR-based prediction model based on “wartime influencing factors - material consumption” and perform model training. In case study, we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach. The results illustrate its higher accuracy and more convenience compared with other current approaches. Taking data acquisition controller prediction as an example, our model has better prediction performance (RMSE = 0.575 7, MAPE (%) = 12.037 6 and R2 = 0.996 0) compared with BP neural network model (RMSE = 1.272 9, MAPE (%) = 23.577 5 and R2 = 0.980 3) and GM (1, 1) model (RMSE = 2.095 0, MAPE (%) = 24.188 0 and R2 = 0.946 6). The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.