عنوان مقاله [English]
In the futures century, it's impossible to imagine a world without air travel. every day airlines bring millions of passengers and goods between different countries and cities. in this process, the safety of flying airplanes has particular importance. different countries require advanced detection systems to identify their aerospace care for all flight targets. In many countries, the discovery process takes place using a variety of radars, and the identification process is carried out in a number of different ways, mainly by individuals specializing in spatial control centers, the main drawback of the current methods is the probability of occurrence of human decision making errors. therefore, it seems that considering the predictions and the increasing trend of air traffic, it is necessary to design fuzzy decision making and artificial intelligence software systems that can be used as decision makers in decision making in this area. accordingly, in this paper, using existing data as training, testing, validation and training information in the environment of the Tacachi-Sugeno interface fuzzy algorithm in MATLAB software environment, an optimal model for detection and identification processes is presented by the proposed system. the most important result of this study is to improve the detection and proper action of the flight targets by approximately 20%. given that the proposed algorithm has the ability to learn and predict trends due to the use of the knowledge base, it will be wider to apply in future missions.