Calculation of the Combined Threat of Air Targets Using Neuro-Fuzzy Systems

In recent years, intelligent structures play the key role in different military fields and take the place of human operators in novel armed services step by step. Threat evaluation of flying air targets in command and control systems is performed by expert operators through their knowledge and experience. Analysis of input data in the data-fusion systems is a very difficult task requiring complicated decision. Capability and accuracy of intelligent systems for threat prediction of flying air targets base on different received parameters can be a great assist in final macking decision. In this study, a neural network and ANFIS regression models are used to determine the priority of the threat of flying air targets in the command and control systems intelligently and instantly. The error of trained neural network and ANFIS for test dataset are 4.14% and 1.65%, respectively indicating superior ability of these structures in threat estimation of flying air targets. Furthermore, relationships among target variables and threat level is studied. Finally, a dynamic battle scene with different flying air targets is simulated and developed moled is validated.