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 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 Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 422 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Fangqing Zhang ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ambient Temperature ◽  
Grey Relational Analysis ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Dissolved Gas ◽  
Gas Concentration ◽  
Relational Analysis ◽  
Mode Decomposition ◽  
Grey Relational

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

scholarly journals Study on selecting of prefabricated plant site basing on BP neural network

2018 ◽  
Vol 53 ◽  
pp. 03073
Author(s):  
Yao Gang ◽  
Yang Yang ◽  
Shen Xin ◽  
Li Jun
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Precision ◽  
Bp Neural Network ◽  
Raw Materials ◽  
Output Factor ◽  
Plant Site ◽  
Selection Of ◽  
Transportation Facilities

In this paper, the evaluation and prediction model of prefabricated plant site was established by BP neural network, which taking nine factors into consideration, such as location, topography, land scale, transportation facilities, availability of raw materials and labour. These nine factors were taken as input factors, and the normalized global value was taken as output factor. The normalized global value was used to evaluate the performance of prefabricated plant site. In addition, the model was verified to be accurate by analysing twelve prefabricated plant site samples. Therefore, it is obvious that the model is stable in operation with high precision, and can provide effective support in the selection of prefabricated plant site.

A NOVEL FLOW-BASED METHOD USING GREY RELATIONAL ANALYSIS FOR PATTERN CLASSIFICATION

2013 ◽  
Vol 12 (01) ◽  
pp. 75-93 ◽  
Author(s):  
YI-CHUNG HU
Keyword(s):  
Grey Relational Analysis ◽  
Learning Algorithm ◽  
Pairwise Comparison ◽  
The Other ◽  
Data Sets ◽  
Classification Problems ◽  
Real World Data ◽  
Relational Analysis ◽  
The Difference ◽  
Grey Relational

Flow-based methods based on the outranking relation theory are extensively used in multiple criteria classification problems. Flow-based methods usually employed an overall preference index representing the flow to measure the intensity of preference for one pattern over another pattern. A traditional flow obtained by the pairwise comparison may not be complete since it does not globally consider the differences on each criterion between all the other patterns and the latter. That is, a traditional flow merely locally considers the difference on each criterion between two patterns. In contrast with traditional flows, the relationship-based flow is newly proposed by employing the grey relational analysis to assess the flow from one pattern to another pattern by considering the differences on each criterion between all the other patterns and the latter. A genetic algorithm-based learning algorithm is designed to determine the relative weights of respective criteria to derive the overall relationship index of a pattern. Our method is tested on several real-world data sets. Its performance is comparable to that of other well-known classifiers and flow-based methods.

Research of the Relationship between Hemp Fiber Disintegration Degree and its Chemical Composition

2012 ◽  
Vol 482-484 ◽  
pp. 2531-2534
Author(s):  
Ying Chao Ji ◽  
Fei Wang ◽  
Yu Jia Liang ◽  
Dan Yue Li
Keyword(s):  
Chemical Composition ◽  
Mathematical Models ◽  
Grey Relational Analysis ◽  
Theoretical Basis ◽  
Hemp Fiber ◽  
Relational Analysis ◽  
Degumming Process ◽  
Grey Relational ◽  
The Relationship ◽  
Selection Of

In order to solve the problem of degumming and short yarn of the hemp fiber, this paper mainly research the relationship between hemp fiber disintegration degree and its chemical composition. Then discuss how the contents of pectin, hemicellulose, cellulose and lignin in hemp fiber could affect the hemp fiber disintegration degree. Calculate and analysis the four factors’ affection degree by Grey Relational Analysis, and build four mathematical models between hemp fiber disintegration degree and each of its content at last. This paper makes a contribution to provide the theoretical basis to the degumming process of hemp fiber and the selection of spinning material.

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