Synthetic sensors enable flight data estimation without devoted physical sensors. Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. The angle of attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g., model-based, data-driven, and model-free state observers. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse, and unbalanced training domain. An alternative is offered by regularization networks, such as radial basis function, to cope with training domain based on real flight data. The present work’s objective is to evaluate performances of a single-layer feed-forward generalized radial basis function network for AoA estimation trained with a sequential algorithm. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data.

中文翻译：

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用于合成攻角估计的神经方法的实验分析

合成传感器无需专用物理传感器即可估算飞行数据。在现代数字航空电子设备中，合成传感器可以实现并用于多种目的，例如分析冗余或监控功能。在空中数据系统级别测量的攻角可以使用合成传感器进行估计，这些传感器利用多种解决方案，例如基于模型、数据驱动和无模型状态观察器。在数据驱动的观察者类中，多层感知器神经网络被广泛用于近似输入-输出映射攻角函数。处理实验飞行测试数据时，多层感知器可以提供可靠的估计，即使噪声、稀疏和不平衡的训练域可能会出现一些问题。正则化网络提供了另一种选择，例如径向基函数，处理基于真实飞行数据的训练域。当前工作的目标是评估单层前馈广义径向基函数网络的性能，用于使用顺序算法训练的 AoA 估计。所提出的分析是通过比较使用采用相同训练和验证数据的多层感知器网络获得的结果进行的。