关键词：
HF-UHF RFID tag antenna
multi-scale convolutional neural network
long-short term memory
return loss
摘要：
High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application ***,it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic(EM)*** overcome this,the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory(MSCNN-LSTM)for predicting the return loss of UHF antennas instead of EM *** the proposed MSCNN-LSTM,the MSCNN has three branches,which include three convolution layers with different kernel sizes and ***,MSCNN can extract fine-grain localized information of the antenna and overall *** LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the *** results show that the mean absolute error(0.0073),mean square error(0.00032),and root mean square error(0.01814)of theMSCNNLSTM are better than those of other prediction *** predicting the return loss of 100UHFantennas,compared with the simulation time of 4800 s for High Frequency Structure Simulator(HFSS),MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy,significantly reducing the calculation time,which provides a basis for the rapid design of HF-UHF RFID tag ***-LSTM is used to determine the dimensions of the UHF antenna *** return loss of the designed dualband RFID tag antenna is−58.76 and−22.63 dB at 13.56 and 915 MHz,respectively,achieving the desired goal.
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