A Fuzzy Neural-Network-Driven Weighting System for Electric Shovel

Authors:
Yingkui Gu;Luheng Wu;Shuyun Tang
Affiliations:
School of Mechanical and Electronical Engineering, Jiangxi University of Science and Technology, Ganzhou, China 341000;School of Mechanical and Electronical Engineering, Jiangxi University of Science and Technology, Ganzhou, China 341000;School of Mechanical and Electronical Engineering, Jiangxi University of Science and Technology, Ganzhou, China 341000
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

In order to improve the weighting precision and optimize the loading of trucks and the production efficiency of electric shovel, an online weighting model is developed by using fuzzy logic and improved T-S neural network in this paper. The weighting model is established based on the mechanics analysis of the electric shovel firstly. Then, a T-S fuzzy neural network model is established to obtain the influence coefficient through training large numbers of samples. Applications show that by using the presented weighting model, it not only can decrease the fuzzy and uncertain factors in the weighting process, but also can improve the production and management efficiency.