###### Abstract  
High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely studied. However, there are still two major problems, namely the training strategy and sensitivity analysis of the flight parameters. For the first problem, the gradient descent method is usually used, which is time-consuming and can easily converge to a local solution. To solve this problem, an extreme learning machine is proposed to determine the weights based on a Moore-Penrose generalized inverse. Moreover, a genetic algorithm method is proposed to optimize the weights between the input and hidden layers. For the second problem, a mean impact value (MIV) method is proposed to measure the sensitivity of the flight parameters, and the neuron number in the hidden layer is also optimized. Finally, based on the measured dataset of an aircraft, the proposed flight load prediction method is verified to be effective and efficient. In addition, a comparison is made with some well-known neural networks to demonstrate the advantages of the proposed method. flight loads; flight parameters; extreme learning machine; genetic algorithm +
Footnote †: journal: Medical  
Although the flight load determined by the flight parameter prediction method may degrade its accuracy, it is still an effective method that is widely used in practical engineering [16]. Currently, many advanced combat aircraft generally use a hybrid method that combines the flight parameter prediction and strain measurement method [17]. That is, most aircraft in a given fleet use flight-parameter-based flight load prediction monitoring, and a few aircraft have strain gauges attached to some key components. It is worth mentioning that the measured strain data are mainly used to establish, verify, and improve the flight-parameter-based flight load prediction method [18]. In the flight-parameter-based flight load prediction method, the multiple linear regression method is often used to establish the relationship between the flight parameters and loads, and then the operational flight load is determined based on the recorded flight parameters during the flight [19]. However, the prediction accuracy based on the multiple linear regression is low, and the nonlinear relationship between flight parameters and loads cannot be revealed and established [20; 21]. To address this issue, various artificial neural networks have been studied to build flight load prediction models that solve the nonlinear mapping problem and improve the accuracy of the flight load prediction [22; 23; 24]. Based on the multilayer perceptron architecture, it was demonstrated that the correlation coefficient values between the predicted and measured load values of the key components of the outer and inner wings were 0.997 and 0.995, respectively, and the errors resulting from fatigue damage life were \(-3.1\%\) and \(2.7\%\), respectively [9; 25]. Later, a model  
Figure 1: Flowchart of individual aircraft fatigue life monitoring. Figure 2: Demonstration of measured bending moment series of aircraft wing during flight. of flight parameters with fuselage frame and leading-edge flap loads was established based on neural network technology [3]. The average error between the life of the fuselage frame and leading-edge flap calculated by the predicted load and the measured life is 21% and 30%, respectively. In addition, a local model network was proposed to overcome the lack of transparency of conventional networks and the robust extrapolation behavior [16; 26]. Based on this model, the flight loads of the bending and torsional moments on the vertical tail plane were accurately predicted using the flight parameters.