scholarly journals The GM-BP Neural Network Prediction Model for International Competitiveness of Computer Information Service Industry

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 308
Author(s):  
Xianhang Xu ◽  
Mohd Anuar Arshad ◽  
Ubaid Ali ◽  
Arshad Mahmood
Keyword(s):  
Prediction Model ◽  
Bp Neural Network ◽  
Industrial Revolution ◽  
Service Industry ◽  
Information Service ◽  
International Competitiveness ◽  
Analysis Method ◽  
Relational Analysis ◽  
Key Indicators ◽  
Grey Relational

The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the critical period of global economic transformation and development, the use of mathematical models to predict its international competitiveness will help scientifically evaluate the development level of the industry and accelerate the adaptation to the needs of the fourth industrial revolution. In this article, a prediction model is proposed for the international competitiveness of the computer information service industry. First, we used the Revealed Comparative Advantage (RCA) index to measure the international competitiveness of the computer information service industry. Furthermore, based on the characteristics of the industry and high-quality development theory, we constructed the evaluation indicator system of influencing factors and used the grey relational analysis method to screen key indicators. Then, we combined the Grey model and BP neural network algorithm to construct the GM-BP prediction model. Finally, China is used as an example to predict the international competitiveness of its computer information service industry, and suggestions are made for industrial development. The results show that the grey relational analysis method can genuinely reflect the impact of different aspects on the international competitiveness of China’s computer information service industry and better determine the key indicators of influencing factors. The GM-BP model has minor errors and excellent simulation results and can accurately predict the future status of international competitiveness. The applicability and reliability of the model are reasonable.

scholarly journals Prediction of Regional Logistics Heat and Coupling Development between Regional Logistics and Economic Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guojun Yin ◽  
Jianhui Peng
Keyword(s):  
Prediction Model ◽  
Bp Neural Network ◽  
Economic Systems ◽  
Regional Logistics ◽  
Relational Analysis ◽  
Two Systems ◽  
The Difference ◽  
Grey Relational ◽  
Selection Of ◽  
Coordination Degree

The research on logistics heat facilitates the understanding of the drivers of regional logistics development. However, many scholars ignore the difference between prediction methods in terms of attributes and focal points of data analysis during the selection of regional logistics heat prediction model. Regional logistics interacts with regional economy. However, the studies on the coupling development between the two systems fail to make a detailed analysis in the light of their actual situation. Therefore, the evaluation of the coordination degree is often biased. To solve the problem, this paper probes into the prediction of regional logistics heat and the coupling development between regional logistics and economic systems. Firstly, an index system was established to measure the level of coupling development between the two systems, and a grey relational analysis was performed on the indices, leading to the evaluation results on coordination degree. Next, a composite model of GM (1, 1) and backpropagation (BP) neural network was proposed, and the deviation interval of the composite predictions was predicted based on Markov chain prediction model. The proposed algorithm proved effective through experiments.

scholarly journals Multi Performance Optimizationof Shoulder Milling Process Parameters of AA6063 T6 Aluminium Alloy by Taguchi Based GRA

2019 ◽  
Vol 8 (10S) ◽  
pp. 420-425
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Analysis Data ◽  
Milling Process ◽  
Depth Of Cut ◽  
Analysis Method ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper, a grey relational analysis method based on Taguchi is proposed to improve the multi-performance characteristics of VMC shoulder milling process parameters in the processing of AA6063 T6. Taking into account four process parameters such as coolant, depth of cut,speed and feed, there are three level of each process parameter in addition to two levels of coolant. 18 experiments were used by L18 orthogonal array using the taguchi method. Multi-performance features like surface roughness and material removal rate are used. Grey Relational Analysis method is used to obtain the Grey Relational Grade, and the multiperformance characteristics of the process are pointed out. Then, the Taguchi response table method and ANOVA are used to analysis data. In order to ensure the validity of the test results, a confirmation test was conducted. The study also shows that this method can effectively improve the multi-function characteristics of shoulder milling process.In his work microstructure and mechanical properties of AA6063 T6before and after shoulder milling have been investigated.

Selection of Molybdenum-Filled Hip Implant Material Using Grey Relational Analysis Method

2018 ◽  
pp. 675-692 ◽  
Author(s):  
Amit Aherwar ◽  
Amit Singh ◽  
Amar Patnaik ◽  
Deepak Unune
Keyword(s):  
Grey Relational Analysis ◽  
Testing Machine ◽  
Base Material ◽  
Analysis Method ◽  
Wear Properties ◽  
Hardness Tester ◽  
Relational Analysis ◽  
Implant Material ◽  
Pin On Disc ◽  
Grey Relational

In this study, a series of implant material containing molybdenum of different weight percentages were fabricated via high temperature vertical vacuum casting induction furnace and examined their physical, mechanical and wear properties. The mechanical properties were tested by the micro-hardness tester and the compression testing machine, while the wear performance was analyzed through a pin-on-disc tribometer under different operating conditions at room temperature. Density, hardness, compressive strength and sliding wear were considered as criterions for this study. The proportions of alternatives consist of Co-30Cr as a base material and molybdenum as an alloying element which was varied from 0 to 4wt.%. Due to the conflict between the properties obtained, the Grey relational analysis method (GRA) was applied to choose the best material among the set of alternatives. From the results obtained, it was found that Co-30Cr implant material containing 4wt.%molybdenum provides the best combination of the properties for a given application (i.e. hip femoral head).

Disturbing Load Classification Based on the Grey Relational Analysis Method and Load Performance Index

2019 ◽  
Vol 31 (1) ◽  
pp. 141-152
Author(s):  
Alexandre C. Moreira ◽  
Wesley A. de Souza ◽  
Bruna R. P. Conrado ◽  
Helmo K. Morales-Paredes
Keyword(s):  
Grey Relational Analysis ◽  
Performance Index ◽  
Analysis Method ◽  
Relational Analysis ◽  
Grey Relational
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