基于BP神经网络的智能轮胎标签仿真研究

为了更加快捷方便地研究RFID标签植入环境与阅读器阅读距离之间的关系,从而预测植入轮胎中的RFID标签的最大阅读距离；利用FEKO电磁仿真软件建立了不同情况下的天线,并仿真得到反射系数S_11,然后用弗林斯传输方程(Friis)计算得到仿真读取距离,再利用MATLAB强大的数据处理能力,建立BP神经网络预测模型,从而建立起标签天线长度、轮胎中标签与钢丝层的距离、轮胎介电常数和已得到的仿真读取距离之间的BP神经网络模型；最后实际测量值与训练后得到的预测仿真值在误差允许的范围内可以认定为实际测量距离；此方法可以通过建立BP神经网络模型,快速方便地在一定精度范围内预测阅读器的阅读距离。

Simulation to Intelligent Tire Tag Based on BP Neural Network

In order to study the relationship between RFID tag implantation environment and Readers read distance,thereby predicting the maximum reading distance of the RFID tag implanted into the tire;the FEKO electromagnetic simulation software established many antennas with different conditions and the reflection coefficient S_11 is obtained, And then use the Frings transfer equation (Friis) to calculate the simulation read distance. In MATLAB, there are neural network toolboxes that can be called, and the BP neural network prediction model is established by using MATLAB''s powerful data processing ability to establish the length of the tag antenna, the distance between the label and the wire layer in the tire, the tire dielectric constant and the simulation of the distance between the BP neural network model.;At last,The actual measured value and the predicted simulation value obtained after training can be regarded as the actual measurement distance within the allowable range of error;Therefore, the BP neural network model can be used to predict the reading distance of the reader within a certain precision range quickly and easily.