Cost Estimation Method for Missile Equipment in the Development Stage Based on Linear Regression - Random Forest Method
This paper addresses the nonlinearity and small sample size characteristics of cost prediction in the missile equipment development stage. By using the linear regression method, 11 cost-related indicators were selected from 22 indicators. A cost estimation model based on the linear regression - random forest method was constructed using 20 sets of historical data from a certain missile development project. The estimation results of the established model were compared and analyzed with those of the neural network model. The root mean square error (RMSE) of the established model was 67.31% lower than that of the neural network model, and the mean absolute error (MAE) of the established model was 63.47% lower than that of the neural network model. This verified the superiority of the linear regression - random forest method in complex nonlinear prediction.
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