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High-precision dynamic logistics weighing system based on GRU-BP algorithm
Published:2024-06-27 author:KANG Jie. Browse: 68 Check PDF documents
High-precision dynamic logistics weighing system based on GRU-BP algorithm


KANG Jie

(School of Mechanical and Electrical Engineering, Sanjiang University, Nanjing 210012, China)


Abstract: Aiming at the problem that the measuring accuracy of dynamic logistics scale was sensitive to load, sampling frequency and belt speed, a highprecision dynamic logistics weighing system was studied. Firstly, using three-factor and five-level orthogonal experimental method, combined with the Pearson correlation test principle, the low-pass Butterworth and Kalman filters were used for filtering and noise reduction of the sensor pressure signals, and the acceleration signals were used as the input signals of the model for the feature compensation. Then, an improved gated recurrent unit model was proposed based on a deep learning algorithm, in which the pressure and vibration temporalized signals in the sampling interval were jointly input into the gated recurrent unit (GRU) model. Finally, the GRU model was improved, and the nonlinear mapping ability of the model was effectively enhanced through the back-propagation (BP) neural network of stacking errors in the output layer. The research results show that the maximum measurement error of this model can be respectively reduced by 16.14%, 27.14% and 76% compared with that of the same type of deep learning model, such as long short term memory (LSTM)neural network, recurrent neural network(RNN) timing model and traditional numerical average model, under various transmission speeds and test goods. It can be used in all kinds of weighing systems.

Key words: deep learning; dynamic measurement system; gate control loop unit(GRU); back-propagation(BP) neural network;vibration compensation; long short term memory(LSTM) neural network; recurrent neural network(RNN)
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