摘要
文章描述了一种用反函数校正传感器非线性误差的方法,阐述了校正原理,提出了利用BP神经网络和遗传算法相结合,拟合传感器传输特性反函数的算法,该算法可将传感器传输特性的非线性模型,改造成为与实际物理过程相一致的不失真的线性模型,给出了一个应用实例,其结果表明,可使传感器的非线性误差有较大的减少。
This paper describe a method that the nonlinear error of the sensor is corrected using a inverse function. The correction principle is expounded. A neural network of using genetic algorithms is showed, the network can close with sensor input and output essence, and the nonlinear model sensor can be retrofitted into a nondistortion linear model that is consistent with the actual physical process. Finally, a applied example is introduced, the experimental results show that sensor nonlinear errors are reduce more than tenfold.
出处
《计算机应用》
CSCD
北大核心
2002年第12期31-33,共3页
journal of Computer Applications
