Abstract
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.
Highlights
The competition of general aviation aircraft production and sales companies mainly comes from the control of aircraft product prices, product quality, and production cycles
According to the above analysis of the fitting effect of the four models, the estimation performance reflected by mean absolute percentage error (MAPE), mean absolute error (MAE), R, and root mean square error (RMSE) is as follows: multiple linear regression (MLR) model (0.115, 10.579, 0.9924, 14.028); stepwise regression (SR) model (0.131, 11.475, 0.9906, 15.604); artificial neural network (ANN) model (0.079, 6.611, 0.9935, 12.628); and principle component analysis (PCA)+ANN model (0.009, 1.222, 0.9999, 1.667)
According to the prediction results of these four models, the estimation performance reflected by MAPE, MAE, R, and RMSE is as follows: MLR model (0.316, 62.03, 0.9326, 72.373); SR model (0.232, 50.173, 0.9371, 62.715); ANN model (0.086, 16.667, 0.9946, 22.716); and PCA+ANN model (0.015, 3, 0.9994, 3.416)
Summary
The competition of general aviation aircraft production and sales companies mainly comes from the control of aircraft product prices, product quality, and production cycles. Having an accurate development cost prediction model to quickly formulate reasonable product sales price is extremely important for good decision-making and improving the competitiveness of companies. Due to the difficulty of data collection, late start time, multi-collinearity between variables, the relevant cost prediction models are limited [2], and there is currently no specific model to forecast the general aviation aircraft cost. Since the 1960s, multiple linear regression (MLR) models have been widely utilized to predict aircraft cost. Researchers have developed many MLR models such as DAPCA, PRICE H and SEER H to predict the aircraft costs [5]. The previous studies [8], [9] have demonstrated that the ANN method can obtain more accurate prediction performance owing to the deep learning ability
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