Frequency Management And EMC Decision Making Using Artificial Intelligence/expert System Technology

Abstract

This paper discusses the development of a prototype Artificial Intelligence/Expert System (AI/ES) capabilit t o perform I i nea r/non I i near frequency ma nag eme n t 8 .e., assignment and confliction-deconfliction analysis) and identify optimum EM1 mitigation techniques to achieve total EMC under certain conditions, for a complex system o f equipments. This new capability adapts AVES technologies and exploits powerful modeling, simulation, analysis, prediction, and monitoring features available within such technologies. The EMC en ineering models are integrated into an AVES "shell" wxich permits the specification of certain boundary conditions using rules, procedures, formulas, and constraint data that govern relevant electromagnetic interactions and effects. This paper also discusses a possible thrust to integrate problem solving tools and techniques with a single AVES shell.