CAJ | 학술논문

通过对智能压力传感器精度的研究，选择基于自适应学习率的 BP 算法设计压力传感器。首先，给出了相应的硬件结构和软件设计，然后用标准的 BP 神经网络和改进的 BP 神经网络分别对压力和温度两个目标参量进行数据融合，进行测量结果显示。通过对测量结果的计算比较，发现利用改进的 BP 神经网络设计的传感器测量精度比标准的 BP 神经网络设计的传感器精度更高。
통과대지능압력전감기정도적연구，선택기우자괄응학습솔적 BP 산법설계압력전감기。수선，급출료상응적경건결구화연건설계，연후용표준적 BP 신경망락화개진적 BP 신경망락분별대압력화온도량개목표삼량진행수거융합，진행측량결과현시。통과대측량결과적계산비교，발현이용개진적 BP 신경망락설계적전감기측량정도비표준적 BP 신경망락설계적전감기정도경고。
Through the study on the precision of intelligent pressure sensor, a pressure sensor is designed by using the BP algorithm based on adaptive vector. First of all, the hardware structure and software design are given, and then using the standard BP neural network and improved BP neural network, the pressure and temperature data are respectively fused. Two target parameters measurement results will be displayed and exactly compared. The results show that using the improved BP neural network sensor measurement accuracy is higher than standard BP neural network.