Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model

In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.

A Novel Vertical Handoff Algorithm Based on Fuzzy Logic in Aid of Grey Prediction Theory in Wireless Heterogeneous Networks

To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy logic with the aid of grey theory and dynamic weights adaptation.The grey prediction theory(GPT) takes 4 sampled received signal strengths as input parameters,and calculates the predicted received signal strength in order to reduce the call dropping probability.The fuzzy logic theory based quantitative decision algorithm takes 3 quality of service(QoS)metric,received signal strength(RSS),available bandwidth(BW),and monetary cost (MC)of candidate networks as input parameters.The weight of each QoS metrics is adjusted along with the networks changing to trace the network condition.The final optimized vertical handoff decision is made by comparing the quantitative decision values of the candidate networks.Simulation results demonstrate that the proposed algorithm provides high performance in heterogeneous as well as homogeneous network environments.

Analyzing China’s OFDI using a novel multivariate grey prediction model with Fourier series

Purpose–With the development of economy,China’s OFDI constantly increase in recent year.Meanwhile,OFDI hasspillovereffectoneconomicdevelopmentandtechnologicaldevelopmentofhomecountry.Thus,accurateOFDI prediction is a prerequisite for the effective development of international investment strategies.The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series.Design/methodology/approach–This paper applied a multivariable grey prediction model,GM(1,N),to forecast China’s OFDI.In order to improve the prediction accuracy and without changing local characteristics of grey model prediction,this paper proposed a novel grey prediction model to improve the performance of the traditionalGM(1,N)modelbycombiningwithresidualmodificationmodelusingGM(1,1)modelandFourierseries.Findings–The coefficients indicate that the export and GDP have positive influence on China’s OFDI,and,according to the prediction result,China’s OFDI shows a growing trend in next five years.Originality/value–This paper proposed an effective multivariable grey prediction model that combined the traditionalGM(1,N)modelwitharesidualmodificationmodelinordertopredictChina’sOFDI.Accurateforecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

A new grey predictionmodel for forecasting the automobiles ownership in China

The average relative simulation and prediction percentage errors of the new model are only 0.092%and 3.023%,respectively.The simulation and prediction errors obtained from the classical GM(1,1)and the DGM(1,1)models are,respectively,2.064%and 6.980%in the first case,and 1.942%and 7.360%in the second.The findings show that the GM(1,1,4)model has the best performance,which confirms the effectiveness of the structure improvement.The new model can enhance the smoothness of the background value and weaken the effects of extreme values in the raw sequence in the model’s performance.Therefore,the simulation and prediction performances of the GM(1,1,4)model are better than those of the traditional grey prediction models.The prediction show that the ownership for automobiles in China will grow rapidly in future.Findings could help the government in formulating adjustments to the industrial structures,and facilitate making rational yield plans for automobile firms.

Fractional derivative multivariable grey model for nonstationary sequence and its application

Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.

Research on Prediction Methods of Energy Consumption Data

This paper analyzes the energy consumption situation in Beijing,based on the comparison of common energy consumption prediction methods.Here we use multiple linear regression analysis,grey prediction,BP neural net-work prediction,grey BP neural network prediction combined method,LSTM long-term and short-term memory network model prediction method.Firstly,before constructing the model,the whole model is explained theoretically.The advantages and disadvantages of each model are analyzed before the modeling,and the corresponding advantages and disadvantages of these models are pointed out.Finally,these models are used to construct the Beijing energy forecasting model,and some years are selected as test samples to test the prediction accuracy.Finally,all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019,and the relevant energy-saving opinions were given.

Improvement and application of GM(1,1) model based on multivariable dynamic optimization

For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.

Control method on serial type pump-valve coordinated electro-hydraulic servo system

In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.

The Novel Triangle MGM(1,m,N)Model and Its Applications

The MGM(1,m,N)model is an effective grey multi-variate forecasting model that considers multiple system characteristic sequences affected by multiplefactors.Nevertheless,it isregularly inaccurate in the application This is because the model requires a strong correlation between the system characteristic sequences That reduces the applicability of the model.To solve this problem,this paper proposes a novel multi-variate grey model.This model does not require a certain correlation between system characteristic sequences and has higher applicability Through numerical integration,a two-point trapezoidal formula,and a recursive method,thetime-response expressions ofthetwo model forms are obtained Some properties of the proposed model are further discussed Finally,the validity of the proposed model is evaluated by using two real cases related to China's invention patent development.Theresultsshow that the novel models outperformother models inbothsimulation and prediction applications.

Research on dynamic load balancing of data flow under big data platform

In the big data platform,because of the large amount of data,the problem of load imbalance is prominent.Most of the current load balancing methods have problems such as high data flow loss rate and long response time;therefore,more effective load balancing method is urgently needed.Taking HBase as the research subject,the study analyzed the dynamic load balancing method of data flow.First,the HBase platform was introduced briefly,and then the dynamic load-balancing algorithm was designed.The data flow was divided into blocks,and then the load of nodes was predicted based on the grey prediction GM(1,1)model.Finally,the load was migrated through the dynamic adjustable method to achieve load balancing.The experimental results showed that the accuracy of the method for load prediction was high,the average error percentage was 0.93%,and the average response time was short;under 3000 tasks,the response time of the method designed in this study was 14.17%shorter than that of the method combining TV white space(TVWS)and long-term evolution(LTE);the average flow of nodes with the largest load was also smaller,and the data flow loss rate was basically 0%.The experimental results show the effectiveness of the proposed method,which can be further promoted and applied in practice.