CAJ | 학술논문

为了提高相对高度测量的精确性，研究并实现了一种基于气压传感器阵列式测量和遗传算法（ GA)优化反向传播（ BP)神经网络数据融合处理的高精度气压式相对高度计，给出了相应的硬件结构和软件设计。结合实验测量的数据和相关文献的数据，从准确性、稳定性和通用性的角度对GA-BP神经网络、传统BP神经网络以及标准计算公式在气压式相对高度计中应用的性能进行了对比分析。研究结果表明，本文提出的基于GA-BP神经网络的相对高度计具有更高的测量精度、更高的稳定性和更好的推广能力，能够满足日常相对高度的实时测量需求。
위료제고상대고도측량적정학성，연구병실현료일충기우기압전감기진렬식측량화유전산법（ GA)우화반향전파（ BP)신경망락수거융합처리적고정도기압식상대고도계，급출료상응적경건결구화연건설계。결합실험측량적수거화상관문헌적수거，종준학성、은정성화통용성적각도대GA-BP신경망락、전통BP신경망락이급표준계산공식재기압식상대고도계중응용적성능진행료대비분석。연구결과표명，본문제출적기우GA-BP신경망락적상대고도계구유경고적측량정도、경고적은정성화경호적추엄능력，능구만족일상상대고도적실시측량수구。
In order to improve the accuracy of the relative height measurement,a high-precision pneumatic relative altimeter has been studied and implemented through pressure sensor-array measurements and data fusion with an op-timizing back propagation ( BP ) neural network algorithm based on genetic algorithm ( GA ) . The corresponding hardware and software designs have been provided as well. Combined with experimental measured data and relevant literature data,the application performances of the GA-BP neural network, the traditional BP neural network and standard formulas in the pneumatic relative altimeter were compared and analyzed in term of accuracy,stability and versatility. The results show that the proposed pneumatic relative altimeter based on GA-BP neural network has higher accuracy, higher stability and better ability to promote, and it can meet the daily needs of real-time measurement for the relative height.