Non-Equidistant Grey Model Based on Background Value and Initial Condition Optimization and Its Application

2021 ◽  
Author(s):  
Ziheng Wu ◽  
Yang Liu ◽  
Cong Li
Keyword(s):  
Grey Model ◽  
Initial Condition ◽  
Background Value ◽  
Model Based ◽  
Condition Optimization

A Multivariable Grey Model Based on Optimized Background Value and its Application to Subgrade Settlement Prediction

2012 ◽  
Vol 256-259 ◽  
pp. 1721-1725 ◽  
Author(s):  
Han Bing Liu ◽  
Yi Ming Xiang ◽  
Huu Hung Nguyen
Keyword(s):  
Prediction Models ◽  
Single Point ◽  
Deformation Monitoring ◽  
Grey Model ◽  
Background Value ◽  
Model Based ◽  
Complex Process ◽  
Forecast Result ◽  
Process System ◽  
Multiple Variables

Subgrade settlement is a complex process system. Commonly used single-point prediction models can’t consider the correlation between the discrete deformation monitoring points, which doesn’t adequately reflect the overall deformation law of subgrade. A multivariable grey model (MGM(1,n)), which is an expansion of the single-point GM(1,1) model for multiple variables, is introduced to resolve the above problem. Aiming at the drawback of background value in the traditional MGM(1,n) model, the functions with non-homogeneous exponential law are used to fit the accumulated sequences for every variable, reconstruct the calculating formula of background value, and gets a new MGM(1,n) model based on optimized background value (OMGM(1,n)). A case study shows that the forecast result of the proposed model is more precise and effective than these of the single-point GM(1,1) model and the traditional MGM(1,n) model for predicting subgrade settlement.

scholarly journals A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 cases

2020 ◽  
Author(s):  
Hoang Anh NGO ◽  
Thai Nam HOANG
Keyword(s):  
Time Series ◽  
Small Time ◽  
Simultaneous Optimization ◽  
Grey Model ◽  
Model Parameters ◽  
Initial Condition ◽  
Bernoulli Model ◽  
New Model ◽  
Background Value ◽  
Novel Model

The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam’s GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.

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.

scholarly journals Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

2012 ◽  
Vol 27 (4) ◽  
pp. 972-987 ◽  
Author(s):  
Yong Wang ◽  
Simona Tascu ◽  
Florian Weidle ◽  
Karin Schmeisser
Keyword(s):  
Horizontal Resolution ◽  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Initial Condition ◽  
Weather Variables ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Model Based ◽  
Surface Weather

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

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
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