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 R^2=0.996 0) compared with BP neural network model(RMSE=1.272 9,MAPE(%)=23.577 5 and R^2=0.980 3) and GM(1,1) model(RMSE=2.095 0,MAPE(%)=24.188 0 and R^2=0.946 6).The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.
Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge.However,one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge.In this paper,we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics,integrating conceptual analysis,conceptual modeling,quantitative modeling and simulation implementation.We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition,thus classifying the related battle damage knowledge logically.Then,we construct the conceptual model of influencing factors of battle damage by using Entity-Relations hip approach,thus describing their interactions reasonably.Subsequently,we extract the important influencing factors by using social network analysis,thus evaluating their importance quantitatively and further clarifying the elements of simulation.Finally,we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input,and obtain simulation model output,i.e.,new battle damage laws knowledge,thus verifying feasibility and effectiveness of the proposed method.The results show that this method can be used to support human decision-making and action.
