Statistical and adaptive approach for verification of a neural-based flight control system

This work presents a combined statistical and adaptive approach for the verification of an adaptive, online learning, sigma-pi neural network that is used for aircraft damage adaptive flight control. Adaptive flight control systems must have the ability to sense its environment, process flight dynamics, and execute control actions. This project was completed for a class in complex adaptive systems at Nova Southeastern University. Verification of neural-based damage adaptive flight control system is currently an urgent and significant research and engineering topic since these systems are being looked upon as a new approach for aircraft survivability, for both commercial and military applications. The most significant shortcoming of the prior and current approaches to verifying adaptive neural networks is the application of linear approaches to a non-linear problem. Advances in computational power and neural network techniques for estimating aerodynamic stability and control derivatives provide opportunity for real-time adaptive control. New verification techniques are needed that substantially increases confidence in the use of these neural network systems in life, safety, and mission critical systems.