English

INTELLIGENCE SYSTEM OF ARTIFICIAL VISION FOR UNMANNED AERIAL VEHICLE

The article considers the method of factor cluster analysis which allows automatically retrain the onboard recognition system of an unmanned aerial system. The task of informational synthesis of an on-board system for identifying frames is solved within the information-extreme intellectual technology of data analysis, based on maximizing the informational ability of the system during machine learning. Based on the functional approach to modeling cognitive processes inherent to humans during forming and making classification decisions, it was proposed a categorical model in the form of a direct graph. According to this model, the algorithmic support of the informationextreme factor cluster analysis is developed. It allows automatically retrain the system when expanding the alphabet of recognition classes. According to this algorithm, the on-board recognition system preliminarily carries out the information-extremal machine learning of recognition classes of relatively low power. When new classes appear, their unclassified structured recognition attribute vectors form additional learning matrixes. After reaching a representational volume, additional learning matrix joins the input le

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