摘要
在油品含水量智能检测系统的开发过程中,使用了射频电容传感器。基于水的介电常数远远大于油的介电常数,因而两者呈现了不同的射频阻抗特性。针对射频电容传感器的非线性特性和在对油品水分检测过程中传感器存在对温度的交叉灵敏度问题,提出了基于L-M算法的多层前向神经网络建立传感器逆模型的二维非线性校正方法。为了保证神经网络训练达到最佳效果,对采集的数据提出了抗脉冲滤波和限幅滤波算法。实验结果验证了上述方法的可行性和实用性。
Radio-frequency capacitance sensor is adopted in system of water-content detection in oil product, which is based on the principle that the dielectric constant of water is much higher than that of oil products, and thus the reflected specific properties are different in the respect of radio-frequency impedance. In order to solve the problems of the non-linearity characteristic of radio frequency capacitance sensor and the sensor＇ s cross sensitivity to temperature in the process of water content detection in oil product, the two-dimension non-linearity compensation of sensor adverse model based on multi-layer forward neural network using L-M arithmetic is brought forward. Anti-pulse mean filtering and the scope limited filtering arithmetic are used on sampled data to insure optimum effect of neural network training. The experiment conclusions validate the method＇ s feasibility and practicability.
出处
《传感器与微系统》
CSCD
北大核心
2006年第9期29-32,共4页
Transducer and Microsystem Technologies
基金
国家自然科学资金资助项目(50275150)
关键词
射频电容传感器
非线性校正
交叉灵敏度
神经网络
抗脉冲滤波
radio frequency capacitance sensor
non-linearity compensation
cross sensitivity
neural network
anti-pulse mean filtering
