Black-Box Modeling of Electromagnetic Interference Effects and Key Port Determination Method in Electronic Systems Based on Bayesian Networks

Abstract

The concept of modeling electromagnetic interference effects in electronic systems with Bayesian networks (BNs) is proposed to transform the complex mapping problem between system port interference response and system effects into a classification problem solved by Bayesian networks. For a specific use case, network structure learning and parameter learning are performed on the basis of discrete processing of raw continuous data. Through the validation of test data, it is demonstrated that the learned network structure meets our expectation of the relationship between electromagnetic environment, ports, and system effects, and has a high accuracy in predicting the system electromagnetic interference effects. Based on this, a Bayesian formula-based port risk quantification method is proposed to assist in identifying critical ports.