A neuro-evolutionary synthesis of coordinated stable-effective compromises in hierarchical systems under conflict and uncertainty

Abstract

Abstract The main principles of neuro-evolutionary technology for hierarchical systems control optimization under conflict and uncertainty are considered. The problem statement is formulated as a hierarchical game under uncertainty with the right first move, which uses the principle of a coordinated stable-effective compromises generalizing the Stackelberg hierarchical equilibrium principle. To construct the optimal control law, the evolutionary synthesis of coordinated stable-effective compromises method is applied. An artificial neural network based on radial-basis functions multi-criteria synthesis method that implements the optimal control law in real time is developed. The problem of a hierarchical FANET-based (Flying Ad Hoc Network) remote monitoring system control optimizing in real time is solved.