Research on civil aircraft cockpit display interface availability considering multidimensional indicators clustering and reduction

The availability of civil aircraft cockpit display interface is related to pilot's satisfaction as well as flight safety and efficiency, but current indicator system in this field lacks target-oriented focus. Firstly, in this paper, the flight mission, pilots' senses, pilots' cognition, and flight interaction dimensions are obtained by executive process interactive control model(EPIC), and extracted usability indicators from these dimensions. Secondly, to tackle the problem of filtering multidimensional indicator datasets supported by small samples, an indicator clustering reduction algorithm is proposed based on hierarchical clustering-rough set information entropy(HC-CEBARKNC) which compared to K-means clustering and genetic algorithm. Finally, the support vector machine(SVM) classification model was employed to verify performance and reliability of both algorithms. The experimental result shows the HC-CEBARKNC algorithm proposed has better evaluation accuracy that contribute to practical indicators reduction and decision rules screening.