Application of RBF Neural Network in Dynamic Weighing

Qing Jiang; Xiao Qian Shen; Jin Hui Cai; Yan Yao

doi:10.4028/www.scientific.net/AMR.383-390.1495

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 383-390Application of RBF Neural Network in Dynamic...

Application of RBF Neural Network in Dynamic Weighing

Abstract:

In order to improve the dynamical respond of the weighing system and to meet the demand of rapid weighing, a new method based on radial basis function neural network (RBFNN) is introduced in this paper. The dynamic system is described as a network and the output values of steady state are predicted by an on-line modeling before the platform has settled to the steady state. The sample weight is calculated according to weighted moving average. The experimental results proved that the neural network method in this paper can be used to effectively reduce the weighing time and to increase the accuracy simultaneously.

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Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

1495-1499

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.383-390.1495

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Online since:

November 2011

Authors:

Qing Jiang, Xiao Qian Shen, Jin Hui Cai, Yan Yao

Keywords:

Dynamic Weighing, Prediction Methods, RBF Network

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References

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