Weighing Fusion Method for Truck Scales Based on Prior Knowledge and Neural Network Ensembles

Abstract

This paper presents a new approach of compensating truck scale's weighing errors based on prior knowledge and neural network ensembles (PKNNEs). Truck scale is a typical nonlinear system and it is fussy and labor intensive to compensate the weighing errors with the conventional method, leading to low accuracy of the weighing results. The general idea of this proposed approach consists of building individual neural networks (NNs) and designing the constraint conditions for optimizing neural network ensembles (NNEs) with the prior knowledge of the truck scale. First, three uncorrelated individual NNs are created by using the step-distribution characteristics of the truck scale's permissible maximum weighing error. Second, the constraint conditions for training the individual NNs are constructed by using the ideal weighing model and its derivatives, which can significantly improve the generalization ability of NNs, especially when the training samples are few or lacking. The detailed design procedure of this proposed method is given, the weighing principle of truck scale is discussed, and its weighing error models are found in this paper. Experimental results demonstrate the effectiveness of this method, and the testing results of a truck scale with PKNNEs in the field show that it meets the requirement for the weighing accuracy of medium-class scale defined by OIML R76 “nonautomatic weighing instruments.”