Abstract
Accurate weighting of pieces in various temperature environments for load cells is a key feature in many industrial applications. This paper proposes a method to achieve high-precision ±0.56/3000 grams for a load-cell-based weighting system by using ANFIS. ANFIS is used to model the relationship between the reading of load cells and the actual weight of samples considering temperature-varying effect and nonlinearity of the load cells. The model of the load-cell-based weighting system can accurately estimate the weight of test samples from the load cell reading. The proposed ANFIS-based method is convenient for use and can improve the precision of digital load cell measurement systems. Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling of load cells and the results show that the proposed ANFIS-based method outperforms some existing methods in terms of modeling and prediction accuracy.
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