Application Of Multivariate Grey Optimization Model For The Forecasting Grain Production Of Shandong Province

Prediction is an important method for people to understand the law of things development,and its purpose is based on objective information and conditions.With the continuous development of science and technology,there have been many models of scientific prediction,including single prediction model and combination prediction model.The single prediction model often depicts the law of data sequence from one side,reflecting the order of sequence.It usually has its limitations.The combined forecasting model can make use of many kinds of forecasting methods to make the best use of the existing information.So,this paper chooses the combined forecasting model to predict the grain yield of Shandong province.The biggest feature of the grey prediction model is the small data modeling.The grey prediction model has its unique advantages in the condition of incomplete and ambiguous information.However,the grey prediction model also has its inherent defects.It can not be very good for the random fluctuation data sequence.The Markov model is a random variable.The dynamic system,which is applicable to the time series prediction problem of random fluctuations,can reveal the randomness of the system affected by various complex factors,and describe the overall change trend of the time prediction from the macro.In order to further optimize the model,particle swarm optimization algorithm is introduced.It is a bionic cluster optimization algorithm,the algorithm needs less adjustment parameters and has a strong global search.Ability is widely used in various optimization problems.In view of the prediction of grain output in Shandong Province,distinguish from other single variable grey models for forecasting,this paper,starting with the multivariable grey model,establishes a multivariable grey model based on integral transformation.On this basis,the Markov method is added to modify the residual,and The particle swarm optimization is introduced to optimize the multivariable grey Markov model of particle swarm optimization.It is different from the inertial factor in the standard particle swarm optimization.The inertia factor is optimized linearly in the optimization model,and the convergence of the algorithm is enhanced.The prediction accuracy of the grain output obtained by other methods can be compared with thepredicted value of grain output obtained by other methods.The prediction accuracy of the model established in this paper is better than that of other models.Therefore,it has a certain practical value.This model is used to predict the grain yield in Shandong Province in the next few years.The grain output of Shandong province is increasing year by year in the next few years.It has a good state of development.It is expected to break through 53 million tons in 2022,and put forward the countermeasures and suggestions to develop and stabilize the grain production in Shandong province.