Secondary-diagonal mean transformation Partial Grey Model based on matrix series

2012 ◽  
Vol 26 ◽  
pp. 168-184 ◽  
Author(s):  
Zhongshi He ◽  
Xin Liu ◽  
Yinong Chen
Keyword(s):  
Grey Model ◽  
Model Based ◽  
Secondary Diagonal ◽  
Matrix Series

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

Trust and grey model based data aggregation algorithm

2021 ◽  
Vol 35 (4) ◽  
pp. 258
Author(s):  
Ni Wang ◽  
Li Li ◽  
Yansui Du ◽  
Jun Wang
Keyword(s):  
Data Aggregation ◽  
Grey Model ◽  
Model Based

The housing demand analysis and prediction of the real estate based on the AWGM (1, N) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Xiong ◽  
Huan Guo ◽  
Xi Hu
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Demand ◽  
Grey Model ◽  
Content Type ◽  
First Order ◽  
Model Based ◽  
Per Capita ◽  
Improved Model ◽  
Grey Relational

PurposeThe purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.Design/methodology/approachThis paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.FindingsThis paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.Practical implicationsThe model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.Originality/valueThe factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

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