Abstract : This paper describes 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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