Nonlinear correction of sensors based on neural network model

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 (El) is 9.6 × 10-6.