The Prediction of Radiation Field Intensity Based on RadTransformer Neural Network
When studying board level radiation interference,the accurate analysis of interference propagation path and extraction of parasitic parameters on the path are challenging due to the extremely high signal frequency and complex routing structure of PCB.As a result,factors in modeling are incom-pletely considered and the simulated radiation field intensity lacks accuracy.To address this issue,we u-tilize a self-developed RadTransformer model with multi-head self-attention neural network com-bined with CST software to swiftly and accurately predict the radiation field intensity of vehicle indicator system.Firstly,we conduct radiation emission tests according to national standard GB/T 18655-2018 and establish a simulation model that replicates the actual test environment for benchmarking purposes.Subsequently,three crucial parameters related to radiation field intensity-common mode current,transfer function,and antenna coefficient-are selected.By employing the RadTransformer model,their characteristic values regarding attention towards radiation field intensity are extracted for predic-ting it at specified frequencies.The research findings demonstrate that the prediction method exhibits both accuracy and efficiency as it only takes 0.1ms for predictions while maintaining an average error value of merely 2.0-a reduction by 30％compared to that of the benchmark model.
万方数据
维普
