Shot Performance Influence Factors Correlation Degree Analysis Based on Genetic Algorithms

2015 ◽  
Vol 9 (1) ◽  
pp. 479-482
Author(s):  
Lei Yang
Keyword(s):  
Genetic Algorithms ◽  
Scientific Evidence ◽  
Influence Factors ◽  
Scientific Training ◽  
Physical Knowledge ◽  
Five Factors ◽  
Correlation Degree ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

The paper first applies Newton mechanical and physical knowledge to establish shot throwing process mathematical model, calculates throwing performance is related to α, h, t0, v0 these five factors; the next, it adopts genetic algorithms to solve optimal throwing performance and these five factors best parameters, from which optimal throwing performance is 21.78m; finally it adopts grey relational degree algorithms and analyzes five influence factorsf primary and secondary relation is α, h, t0, v0 , which provides scientific evidence for making scientific training plans, during training, coaches and athletes should pay attention to foster strengths and circumvent weakness, give their own advantages into full play so that can get more ideal results.

A new grey comprehensive relational model based on weighted mean distance and induced intensity and its application

2019 ◽  
Vol 9 (3) ◽  
pp. 374-384 ◽  
Author(s):  
Kedong Yin ◽  
Jie Xu ◽  
Xuemei Li
Keyword(s):  
Water Resources ◽  
Relational Model ◽  
Weighted Mean ◽  
Domestic Water ◽  
Content Type ◽  
Correlation Degree ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational ◽  
Degree Of Similarity

Purpose The purpose of this paper is to study the essential characteristics of grey relational degree of proximity, to analyse the abstract meaning of grey relational degree of similarity and fully consider the two different relational degree models. Design/methodology/approach The paper constructed the grey proximity relational degree by using the weighted mean distance. To analyse the motivation of the development of things, this paper constructed the grey similarity degree by using the concept of induced strength. Finally, the two correlation models are weighted by reliability weighting. Findings The research finding shows that the distance is the essence of the grey relational degree of proximity, and the induced strength is a good explanation of the similarities in the development of things. Practical implications The analyses imply that the total amount of water consumption in China has the greatest correlation with the consumption of agricultural water resources, followed by the consumption of industrial water resources, and the least correlation with the consumption of domestic water resources. Originality/value The paper succeeds in realizing the essential characteristics of grey relational degree of proximity and the abstract meaning of grey relational degree of similarity. Besides, the resolution of the correlation degree can be greatly improved by reliability weighting.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

The Grey Relational Analysis of Driving Forces for Water Consumed Structure

2012 ◽  
Vol 490-495 ◽  
pp. 1612-1616 ◽  
Author(s):  
Cui Mei Lv ◽  
Fa Xing Du
Keyword(s):  
Grey Relational Analysis ◽  
Driving Forces ◽  
Water Saving ◽  
Utilization Rate ◽  
Sustainable Utilization ◽  
Urbanization Level ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

Grey Relational Analysis is a method of analysis and calculates the relational degree of evaluated object, which can characterize the relational degree between object with viral object. In this paper it was used to analyze the driving forces of water consumed structure change, and YiChang city was selected as an example. Adopted grey relational degree analysis, the main factors were found out. The results showed that industry water utilization rate, irrigation area, urbanization level are the main driving forces, and corresponding water-saving measures were put forward. This study can provide reference for the construction of water-saving society and sustainable utilization of water resource.

scholarly journals Study on the Relatedness between Arterial Highway Freight Volume and GDP

2020 ◽  
Vol 7 (3) ◽  
pp. p92
Author(s):  
Yuhan Li
Keyword(s):  
Freight Transport ◽  
Practical Significance ◽  
Actual Condition ◽  
Actual Situation ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Calculation Results ◽  
Freight Volume ◽  
Grey Relational ◽  
Municipal Level

There is a deviation from the actual condition of freight transport and ‘volume of freight transport by vehicles’ counted by the current highway and waterway industry statistical statement system of China at the provincial or municipal level. This paper puts forward the concept of ‘volume of freight transport in regions’, uses survey data and administrative data to calculate the Grey relational degree between arterial highway freight volume and the GDP of the three industries. The quantitative analysis of the calculation results shows that the results are consistent with the actual situation, which is of certain practical significance. The variation trend of the arterial highway freight volume can reflect the economic development of the region.

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