USING ARTIFICIAL NEURAL NETWORKS AND SELF-ORGANIZING MAPS FOR DETECTION OF AIRFRAME ICING

A method of using Artificial Neural Networks (ANNs) and Kohonen Self-Organizing Maps (SOMs) to detect ice on a horizontal tail is proposed and investigated. It is hypothesized that ANN systems trained on the aircraft dynamics would converge to different connection weights for iced and clean aircraft. Kohonen SOMs are used to detect these differences automatically and therefore identify the configuration as being clean or being iced. The system is shown to be capable of acting in an advisory role for the flight crew. The fidelity of the approach is shown to depend on the level of atmospheric turbulence, as well as the magnitude of the elevator input NOMENCLATURE Subscripts denote derivatives, unless stated otherwise. [A] state matrix [B] control matrix

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