A novel life prediction method of RF circuits based on the improved recurrent broad learning system

Abstract

Life prediction of RF circuits can greatly improve the reliability of RF systems. But most literature has been focused on the life prediction of RF devices, which is not applicable to RF circuits. Therefore, a novel RF circuit life prediction method is proposed based on an improved recurrent broad learning system (RBLS). First, a new feature matrix is proposed to characterize a RF circuit at each moment. Then, an improved RBLS model is used to predict the life of the circuit, in which the RBLS model is used to predict the feature vector and then the extreme learning machine (ELM) is used to expand the feature vector into a feature matrix. The method is validated in a low-noise amplifier with classical GRU, BLS-ELM, BLS, RBLS, ELM, and LSTM as a control group. The analysis results show that RBLS-ELM has the highest prediction accuracy with an RMSE of only 2.5959, the smallest prediction uncertainty of 1.2767, and a very short prediction time of 0.4251 s.