## 4 Design of Supplementary Experimental Conditions Based on a BPNN  
If the data fail to pass the above test, it indicates that the completeness of the semi-physical simulation test data is insufficient. A proxy model of the semi-physical simulation system needs to be constructed according to the simulation data. The model is verified and corrected through field test data, and then the proxy model is used to invert the additional data that need to be supplemented. For the semi-physical simulation test conditions, the supplementary test condition data are used to retest and obtain a complete semi-physical simulation dataset to ensure that the two data sources to be fused have a good fusion foundation. Regarding the further prediction of the coordinates of landing points, the prediction models include two categories. The first category is traditional, based on probability theories such as time series models and regression models; the second category is new, based on new algorithms such as machine learning, which mainly include neural networks, intelligent algorithms, and data mining. These new prediction models are mainly aimed at nonlinear systems and the prediction accuracy is higher than that of traditional models. The first type of model relies on the relevant principles of statistics for analysis, which requires the sample data to be relatively clear, and the sample data have a good distribution law. The second type of model is based on nonlinear systems. In the context of big data, new intelligent algorithms and models can make fast and effective predictions. Considering that traditional prediction models have too many constraints and strict requirements for samples, it is generally difficult to obtain better prediction accuracy. Figure 3: Diagram of the completeness judgment of the semi-physical simulation landing point data. Therefore, this paper establishes a semi-physical simulation system proxy model based on a BPNN and uses the neural network method for prediction. A BPNN has excellent performance, and the prediction accuracy is better. In the learning process, the input data of the semi-physical simulation system are input into the input layer, and the result is transmitted to the output layer after being processed by a hidden layer. If the result does not match the output of the actual semi-physical simulation system, the error is backpropagated to the hidden layer and the corresponding weights and thresholds are modified. After the learning process is complete, the verification phase begins. First, the field data are input, and then the results obtained by the neural network are compared with the actual situation and the model is revised. Figure 4 shows a BP neuron model with \(d\) inputs and \(n\) outputs. In the figure, \(x_{1},x_{2},\cdots,x_{d}\) is the input vector of the neuron; \(y_{1},y_{2},\cdots,y_{m}\) is the output vector of the hidden layer neuron; \(v_{ih}\) is the input layer weight link; \(w_{hi}\) is the hidden layer weight link; \(a_{m}\) is the bias of the hidden layer neuron; \(o_{1},o_{2},\cdots,o_{n}\) is the output of the output layer; \(b_{n}\) is the output layer neuron bias; and the excitation function \(\varphi\) is a sigmoid transfer function. Considering that if the large sample and small sample data are directly mixed as the input to the BPNN, the information of the small sample data will be lost due to the difference in sample capacity. Therefore, the data processing needs to be consistent with the sample capacity. Using the data expansion theory in Section 3.2 and based on the bootstrap resampling method, the external field test samples are expanded to be consistent with the semi-physical simulation data sample capacity. The expanded field test samples and semi-physical simulation samples are used as the input to the BPNN. There are many test conditions affecting the landing point of an aircraft, mainly including launch conditions and environmental conditions.