摘要
为了改善传感器的动态特性,减小系统测量误差,分析了传感器动态性能补偿的基本原理,提出了一种基于改进型遗传算法(IAGA)和RBF神经网络相结合的补偿算法,给出了用IAGA-RBF补偿算法建立的数学模型,并应用到瓦斯传感器的补偿环节。实验证明,该补偿算法具有响应速度快、计算精度高和工作频带宽的特点,多项动态特性指标都得到了较大的改善,能够有效地用于传感器的动态特性补偿。
To improve the sensor’s dynamic performance, and reduce errors in systematic measurement, the principle of sensor’s dynamic performance compensation is analyzed. A kind of improved genetic algorithm（IAGA） and the RBF（Radial Basis Function） neural network in the compensation algorithm is proposed. The mathematical model with the IAGA-RBF compensation algorithm is given and applied to the gas sensor compensation unit. Experiments show that the compensation algorithm is of fast response, high accuracy and wide working band, a number of dynamic indicators are also largely improved, which can effectively compensate for the dynamic performance of the sensor.
出处
《传感技术学报》
CAS
CSCD
北大核心
2010年第9期1298-1302,共5页
Chinese Journal of Sensors and Actuators
基金
安徽高校省级自然科学研究重点项目资助(KJ2010A084)
关键词
传感器
动态特性补偿
遗传算法
RBF神经网络
sensor
dynamic compensation
Genetic Algorithm
radial basis function neural network
