摘要
分析了硅 蓝宝石(SOS)压力传感器的温度特性,表明测量范围较宽时,传感器的输出易受环境温度的影响,并且成非线性·提出一种基于神经网络共轭梯度算法的硅 蓝宝石压力传感器温度补偿方法·利用神经网络共轭梯度算法具有逼近任意非线性函数的特点,通过训练使神经网络建立在不同环境温度下传感器输出与其实际感受的电压值之间的非线性映射关系,实现硅 蓝宝石压力传感器温度补偿·计算机仿真表明,该方法不仅能有效地消除温度的影响,而且能在神经网络的输出端得到期望的线性输出·
The temperature characteristics of SOS pressure sensor was analyzed and found that the sensor output is linear and easy to be affected by ambient temperature over a wide measuring range. Based on the conjugate gradient algorithm for neural network,a temperature compensation method for sensor is put forward the way the approximability of the algorithm to any nonlinear function is utilized to drill the neural network so as to enable it to be set up at different ambient temperatures, thus allowing the sensor output can be in a nonlinear mapping relation to the voltage values the sensor actually sensed . The simulation results on computer showed that the method can not only eliminate the influence of ambient temperature fluctuation but obtain the expected linear output from the output terminal of neural network.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第2期160-163,共4页
Journal of Northeastern University(Natural Science)
基金
黑龙江省'九五'攻关项目(G98A10 3)
关键词
硅-蓝宝石压力传感器
环境温度
非线性
神经网络
共轭梯度算法
补偿
SOS(silicon on sapphire) pressure sensor
ambient temperature
non-linearity
neural network
conjugate gradient optimum algorithm
compensation
