scholarly journals The Inventory Control System of Reverse Logistics for E-Commerce Packaging Recovery Based on BP Neural Network

2022 ◽  
Vol 16 ◽  
pp. 413-425
Author(s):  
Zhidan Qin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Inventory Control ◽  
Bp Neural Network ◽  
Reverse Logistics ◽  
Application Process ◽  
Before And After ◽  
Operation Algorithm ◽  
Hardware Configuration ◽  
Reverse Logistics Management

The paper combines BP neural network to optimize the control system of e-commerce packaging and reverse logistics inventory. Through improving the hardware configuration structure of the system, the system can be improved and the operation effect of the system can be improved. The software flow and operation algorithm of the storage control system of e-commerce packaging recycling reverse logistics are optimized step by step, and the logistics is delivered by following the vehicle on the spot and visiting the logistics The distribution personnel collect the relevant data and data in the process of logistics and transportation, draw the reverse logistics business flow chart, point out the situation of reverse logistics before and after the goods distribution and distribution due to the cancellation of orders or transactions by customers, and the application for return of goods after the transaction. Meanwhile, it points out that the sales return operation site in the reverse logistics management process is chaotic and not formed the clear business process specification and other problems can effectively control the reverse logistics inventory of e-commerce packaging recovery. Finally, the experiment proves that the e-commerce packaging recycling reverse logistics inventory control system is more practical in the practical application process, and fully meets the research requirements.

scholarly journals An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Convergence Rate ◽  
Inventory Control ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Inventory Level ◽  
Bp Neural Network Model

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Paste-filling weighing control system optimization based on neural network

2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Back Propagation ◽  
System Optimization ◽  
Filling Material ◽  
Content Type ◽  
Control Precision ◽  
Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

Smith Predictor Based on Predicting Induced-Delay

2011 ◽  
Vol 201-203 ◽  
pp. 2003-2006
Author(s):  
Shu De Li ◽  
Yi Chen ◽  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Control System ◽  
Bp Neural Network ◽  
Networked Control System ◽  
Smith Predictor ◽  
Networked Control ◽  
Object Model ◽  
Random Delay

Since communication network is introduced into control system, induced-delay appears. Because of the delay, the performance of networked control system becomes bad, even unsteady. Conventional Smith predictor is sensitive to error in object model and needs delay’s value in advance. Regarding random delay, its application is limited. In this paper, we propose a method based on induced-delay predicted by BP neural network, which use two historical delay values to predict the next one. Smith predictor adjusts its parameters according to that value in time. The simulating results indicate that the precision of delay-predicting can be ensured and the performance of networked control system has been improved.

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