Complex equipment cost estimation model based on similarity weight

In order to further improve the accuracy of cost estimation for large and complex equipment, such as spacecraft and weapon systems, the large and complex equipment is regarded as a system distribution of a certain parameter set. Define the Jensen-Shannon (JS) divergence, grey correlation, and comprehensive similarity between the tested equipment and the equipment samples, and calculate the sample weight based on the similarity to construct a weighted regression model for cost estimation of complex equipment. To create the cost driving impact matrix, the sample with the highest complete similarity is chosen as the benchmark sample when the sample size does not satisfy the requirements of the least squares modeling. Based on the JS divergence between the parameters and the cost in the matrix, the parameters with larger divergence are selected as the independent variables for the prediction model. By comparing two scenarios in which the sample size is larger and smaller than the parameter size, the comparative analysis demonstrates the excellent prediction accuracy and stability of the similarity weighted regression calculation model based on the combination of JS divergence and grey correlation degree.