A Method for Nonlinear Error Compensation of Load Cell Based on Neural Network With Second Derivative Constraints

Abstract

This paper presents a method for compensating the load cell's nonlinear error based on neural network with the second derivative constraints. In this method, for improving the ability of the neural network in the case of lack of samples, the prior knowledge of load cell, i.e., the second derivative of the load cell's input-output function is less than zero, is used to construct the constraint of neural network. The augmented performance index of training neural network with penalty function method is founded, and its detailed training algorithm is given. In addition, the performance of neural network affected by the punishing factor is discussed. This proposed method can reduce the NN's generalization error when the training samples are insufficient. The experimental results show that the generalization ability of this proposed neural network is better than that of the conventional data inducing neural network (DINN, i.e. training neural network by only using data samples and not any constraints), and the nonlinear error of load cell with this proposed method is far less than that of DINN.