Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

The major objective of this paper is to build a cost prediction model for general aviation aircraft using artificial neural network (ANN) and principle component analysis (PCA) methods. A total number of 22 samples of general aviation aircraft collected from the literature are utilized to train and test the model. In the PCA, eigenvalues of PC1 and PC2 are 6.987 and 1.529, respectively, indicating that they have the strongest interpretation of the original variable information and are retained as cost influencing variables to train the ANN model. The pure multiple linear regression (MLR), stepwise regression (SR) and ANN models are built respectively for comparison. The comparative results reveal that the ANN method has better estimation effect than MLR and SR models in case of multi-collinearity of data. Combined with PCA, the ANN model is optimized, with MAPE, MAE, R and RMSE values of training and testing samples to be 0.009 and 0.015, 1.222 and 3, 0.9999 and 0.9994, 1.667 and 3.416, respectively. Finally, a more accurate and practical prediction model is developed. More importantly, this research can provide an important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategy.