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用遗传网络校正传感器非线性误差的研究
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2009-06-30
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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。
关键词:传感器;非线性误差;反函数;神经网络;遗传算法
Abstract : 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 non2distortion 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.
Key words : sensor ;linear error ;inverse function ;neural network ;genetic algorithms
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。
关键词:传感器;非线性误差;反函数;神经网络;遗传算法
Abstract : 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 non2distortion 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.
Key words : sensor ;linear error ;inverse function ;neural network ;genetic algorithms
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