探空仪湿敏电容器的误差校正模型研究

Research on error calibration model of radiosonde humicap

为了减小探空仪湿敏电容器在高空大气,特别是低温环境下的测量误差,设计了一种基于改进型pi-sigma模糊神经网络的误差校正模型,采用了K-means聚类算法和权值直接确定法提高了网络性能。通过实际测试和BP神经网络进行比较,结果显示:pi-sigma模糊神经网络和BP神经网络对于-30～40℃的144组训练样本的最大相对误差分别为4.774%,15.27%,收敛时间分别为0.01,2 s。4组检验样本结果证明:pi-sigma模糊神经网络有效实现了湿敏电容器在低温条件下的温度补偿和非线性校正,同时在预测精度、泛化能力以及训练速度上均优于BP神经网络。

A modified pi-sigma fuzzy neural network error calibration model is designed to decrease measurement error of radiosonde humicap in upper atmosphere especially in low temperature environment. K-means clustering algorithm and weights direct determination method is implemented to improve network performance. Practical test result is compared with BP neural network, it shows that the maximum relative error of 144 groups of train samples at temperature of are 4. 774 %, 15.2 % respectively, convergence time are 0.01,2 s respectively. Result of 4 groups of test samples demonstrates that pi-sigma fuzzy neural network effectively realizes temperature compensation and nonlinear correction, and is prior to BP network in predicting precision, generalization ability and training speed.