An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

Abstract

Transceiver is a crucial component of radar system that allows for the regulation of signal phase and amplitude as well as the amplification of both transmitted and received signals. Its operational efficiency has a significant impact on the whole dependability of the radar system. To ensure the safe and reliable operation of the radar system, an optimized sparse deep belief network with momentum factor is developed to diagnose potential faults of radar transceivers. Firstly, a momentum term is added into the parameter update to enhance the anti-oscillation ability of model parameters in training, while a sparse regular term is integrated into the deep belief network to prevent the model from overfitting. Secondly, to automatically configure the model hyper-parameters, a hybrid sine cosine algorithm (HSCA) with dynamic inertia weight and adaptive strategies is proposed. Thus, an effective diagnostic model named HSCA-MS-DBN is formed by combining sparse deep belief network with momentum factor and HSCA. The efficiency of the proposed HSCA-MS-DBN model is confirmed using an actual-world radar transceiver dataset, and the findings from experiments reveal that this model surpasses multiple prominent intelligent models.