State Evaluation of Sight Control Box Based on Rough Set Fusion Improved FCM Clustering Algorithm

Abstract

The mechanism of the sight control box of armored equipment is complex and the majority of signals are superimposed and integrated. It is difficult to evaluate the operating state from the perspective of analyzing a single signal. Establishing the state control system of the scope control box by using the rough set fusion FCM (Fuzzy C-Means) clustering algorithm. Firstly, the basic knowledge of rough set and FCM clustering algorithm is introduced, and the FCM clustering algorithm is improved. Since the signal index of the insertion and superposition in the scope control box is too large, only the output signal of the scope control box is selected as the evaluation feature quantity. Secondly, the FCM clustering algorithm is used to fuzzily divide the evaluation feature quantity and simultaneously generate the running state decision table. The rough set attribute reduction method of the difference matrix is used to attribute the attribute quantity of the extracted original data. Finally, through the blind deletion value reduction method, the reduction decision table is attributed to the attribute reduction to obtain a complete reduction decision table, and based on this, a state evaluation rule table is established. Through an example analysis, it is verified that the evaluation model established by the rough set fusion FCM clustering algorithm can accurately and effectively evaluate the operating state of the mirror control box.