Back propagation (BP) neural network models are applied to correct nonlinear characteristics of sensors in this paper. Two sensors of the same type are used to measure two interrelated measurands and their outputs are put into the trained neural network model to obtain linear input-output characteristics. A Recursive Prediction Error(RPE) algorithm, which converges fast, is applied to train the neural network model. As an example, a correction method based on BP is applied to reduce the nonlinear output errors of range sensors. Experimental results show that linear input-output characteristics can be obtained by connecting the trained neural network model with the range sensors. The correction precision increases with the increasing number of nodes in the hidden layer. When the number of nodes in the hidden layer is 40 and the neural network model converges in about 100 iterations,the Error Index(EI) is 9.6×10-6.%讨论了BP神经网络模型在传感器非线性补偿中的应用.给出了相应的补偿方法,即采用两个相同的传感器对同一被测量进行不同的测量,其测量结果作为神经网络模型的输入,经过补偿后的传感器具有线性的输入输出关系.采用递推预报误差算法(PRE)训练神经网络,具有收敛速度快、收敛精度高的特点.以距离传感器为例,将基于BP神经网络的校正方法应用于减少距离传感器的非线性输出误差.实验结果表明,将训练后的神经网络接入距离传感器可以得到线性的输入-输出关系,增加神经网络隐层节点的数目可以提高校正精度.当隐层节点数取为40时,用于距离传感器非线性校正的神经网络模型在训练100步后的误差指数(EI)为9.6×10-6.结果表明:本文提出的基于神经网络模型的传感器非线性校正方法是行之有效的.
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