Error compensation for truck scale based on complex BP neural networks

Haijun Lin; Zhaosheng Teng; Rangzhou Liu; Dan Zheng; Jinbao Yang

doi:10.1117/12.885366

28 December 2010 Error compensation for truck scale based on complex BP neural networks

Haijun Lin, Zhaosheng Teng, Rangzhou Liu, Dan Zheng, Jinbao Yang

Proceedings Volume 7544, Sixth International Symposium on Precision Engineering Measurements and Instrumentation; 75445V (2010) https://doi.org/10.1117/12.885366
Event: Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 2010, Hangzhou, China

Abstract

Truck scale is widely applied to many fields such as storage, trade, transport, communication, industry and mine. Conventional method of error compensation for truck scales is fussy and the accuracy of weighing result is low. An error compensation method based on complex BP neural networks (CBPNNs) is proposed in this paper. Considering the character of error in scale's different weighing zones, the sub-BPNNs are constructed, and each one is used to compensate the weighing error of a weighing zone. The adaptive choice network is founded to automatically choose suitable sub-BPNN, and then the chosen sub-BPNN optimally compensates the error of different weighing ranges. Emulational experiments show that the weighing error of the truck scale with this proposed method is less than that of using a single RBFNN to compensate the error of total weighing range, and all results of field verification show that the maximum eccentric error is -4kg and the maximum repeatability error is +6kg, which is far less than that of the 3th class scales defined by International Recommendation OIML R76 "Nonautomatic Weighing Instruments".

Citation Download Citation

Haijun Lin, Zhaosheng Teng, Rangzhou Liu, Dan Zheng, and Jinbao Yang "Error compensation for truck scale based on complex BP neural networks", Proc. SPIE 7544, Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 75445V (28 December 2010); https://doi.org/10.1117/12.885366

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