Abstract
The cost estimate plays an important role in cost control and developing new products at the design stage. To improve the accuracy of cost estimate, we extract the feature parameters using the theory of concurrent engineering and factor analysis. Then we propose DCEM that is the model of cost estimate based on factor analysis and BP neural network. The model not only simplifies the input of BP neural network, but also avoids the coupling among the input parameters. The result shows that the model’s performance is stable and it can estimate the cost more accurate at the early product design stage.
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