Application and comparison of neural networks and optimization algorithms as a virtual angle of attack sensor

In recent years, we have witnessed the rise of deep learning. Deep neural networks have proved their success in many areas. However, the optimization of these networks has become more difficult as neural networks going deeper and datasets becoming bigger. Therefore, more advanced optimization algorithms have been proposed over the past years. In this study, widely used optimization algorithms for deep learning are examined in detail. To this end, these algorithms called adaptive gradient methods are implemented for both supervised and unsupervised tasks. The behavior of the algorithms during training and results on four image datasets, namely, MNIST, CIFAR-10, Kaggle Flowers and Labeled Faces in the Wild are compared by pointing out their differences against basic optimization algorithms.

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Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks

2019 International Artificial Intelligence and Data Processing Symposium (IDAP) ◽

10.1109/idap.2019.8875929 ◽

2019 ◽

Author(s):

Ebubekir Seyyarer ◽

Taner Uckan ◽

Cengiz Hark ◽

Faruk Ayata ◽

Taner Inan ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Optimization Algorithms

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Angle of attack prediction using recurrent neural networks in flight conditions with faulty sensors in the case of F-16 fighter jet

Complex & Intelligent Systems ◽

10.1007/s40747-021-00612-6 ◽

2021 ◽

Author(s):

Bemnet Wondimagegnehu Mersha ◽

David N. Jansen ◽

Hongbin Ma

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Velocity Measurement ◽

Short Term Memory ◽

Mean Squared Error ◽

Angle Of Attack ◽

Critical Parameters ◽

Rate Measurement ◽

Output Layer ◽

Total Velocity

AbstractThe angle of attack (AOA) is one of the critical parameters in a fixed-wing aircraft because all aerodynamic forces are functions of the AOA. Most methods for estimation of the AOA do not provide information on the method’s performance in the presence of noise, faulty total velocity measurement, and faulty pitch rate measurement. This paper investigates data-driven modeling of the F-16 fighter jet and AOA prediction in flight conditions with faulty sensor measurements using recurrent neural networks (RNNs). The F-16 fighter jet is modeled in several architectures: simpleRNN (sRNN), long-short-term memory (LSTM), gated recurrent unit (GRU), and the combinations LSTM-GRU, sRNN-GRU, and sRNN-LSTM. The developed models are tested by their performance to predict the AOA of the F-16 fighter jet in flight conditions with faulty sensor measurements: faulty total velocity measurement, faulty pitch rate and total velocity measurement, and faulty AOA measurement. We show the model obtained using sRNN trained with the adaptive momentum estimation algorithm (Adam) produces more exact predictions during faulty total velocity measurement and faulty total velocity and pitch rate measurement but fails to perform well during faulty AOA measurement. The sRNN-GRU combinations with the GRU layer closer to the output layer performed better than all the other networks. When using this architecture, the correlation and mean squared error (MSE) between the true (real) value and the predicted value during faulty AOA measurement increased by 0.12 correlation value and the MSE decreased by 4.3 degrees if one uses only sRNN. In the sRNN-GRU combined architecture, moving the GRU closer to the output layer produced a model with better predicted values.

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