Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network
With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing ind...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/8572424 |
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| _version_ | 1832549417868066816 |
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| author | Quan Quan Zhongqiang Zhang |
| author_facet | Quan Quan Zhongqiang Zhang |
| author_sort | Quan Quan |
| collection | DOAJ |
| description | With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO-BP neural network is constructed. Through the collected sample data, the network is trained and simulated, and the results are analyzed. Finally, the designed model is applied to a large battery manufacturing enterprise, and the supplier efficiency evaluation method based on DPMPSO-BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible. |
| format | Article |
| id | doaj-art-0042b32089d845c6a5b6135d86f30faf |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-0042b32089d845c6a5b6135d86f30faf2025-02-03T06:11:18ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/8572424Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural NetworkQuan Quan0Zhongqiang Zhang1China University of Mining and TechnologyChina University of Mining and TechnologyWith the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO-BP neural network is constructed. Through the collected sample data, the network is trained and simulated, and the results are analyzed. Finally, the designed model is applied to a large battery manufacturing enterprise, and the supplier efficiency evaluation method based on DPMPSO-BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible.http://dx.doi.org/10.1155/2022/8572424 |
| spellingShingle | Quan Quan Zhongqiang Zhang Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network Journal of Mathematics |
| title | Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network |
| title_full | Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network |
| title_fullStr | Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network |
| title_full_unstemmed | Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network |
| title_short | Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network |
| title_sort | supply capability evaluation of intelligent manufacturing enterprises based on improved bp neural network |
| url | http://dx.doi.org/10.1155/2022/8572424 |
| work_keys_str_mv | AT quanquan supplycapabilityevaluationofintelligentmanufacturingenterprisesbasedonimprovedbpneuralnetwork AT zhongqiangzhang supplycapabilityevaluationofintelligentmanufacturingenterprisesbasedonimprovedbpneuralnetwork |