Abstract
Purpose: The purpose of this paper is to develop a flexible design-oriented development cost method for general aviation aircraft based on small sample and poor information. Design/methodology/approach: To predict the development cost of general aviation aircraft accurately, the methodology is based on the collected cost data and actual technical, and then the cost prediction relationships derived from an exhaustive statistical and filtered from regression analysis are incorporated. A series of regression equations with high regression coefficient are yielded after the cost driving factors of the development cost are fixed. Next, several sets of equations with high regression coefficient are selected for final integration. It is a flexible method that can be used efficiently to predict the cost of general aviation aircraft. Findings: The development of general aviation aircraft has relatively a late start and no cost prediction model has been suitable for small sample, the proposed method is expected and is rather desirable. Practical implications: By comparing the approach with the ordinary regression model and back propagation (BP) neural network, the scheme in this work is more efficient and convenient. Originality/value: The results obtained in this paper show that the proposed method not only has a certain degree of versatility, but also can provide a preliminary prediction of the development cost of general aviation aircraft.
| Original language | English |
|---|---|
| Pages (from-to) | 567-574 |
| Number of pages | 8 |
| Journal | Aircraft Engineering and Aerospace Technology |
| Volume | 91 |
| Issue number | 4 |
| DOIs | |
| State | Published - 17 May 2019 |
Keywords
- BP neural network
- Development cost
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