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| Design of Neural Network Algorithm for Dynamic Weighing of Belt Weigher |
| DONG Xiang-chen1,LI Bing-ying1,2,LI Yong-xin1,WANG Hai-tao2 |
1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
2. Jiangsu Institute of Metrology, Nanjing, Jiangsu 210023, China |
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Abstract To improve the weighing precision of the belt weigher, it was proposed to introduce process neural network (PNN) to compensate the dynamic weighing error of belt weigher. The weight per unit length of the belt weigher, speed of belt, and variation in belt sag in dynamic weighing process were used as model input. The single hidden layer PNN error back propagation learning algorithm was designed to apply to the study of the dynamic weighing error of the belt weigher. The algorithm model was trained and tested by MATLAB software and the model achieved network accuracy requirements after 149 learning optimizations. The test group error reaches 1%, which is significantly lower than the original error before using the network, and verifies the feasibility and effectiveness of the algorithm.
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Received: 11 September 2018
Published: 14 May 2020
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2020, Vol. 41 
