精英遗传改进的非线性灰色神经网络算子与军费开支多目标组合预测应用

军费开支属于复杂经济系统下具有宏观经济特征的一类非线性时间序列。在多目标组合下的军费开支预测问题研究背景下,提出了一种基于精英遗传算法(elite genetic algorithm,EGA)改进的非线性灰色神经网络计量组合预测模型,给出了总体建模思路与非线性灰色神经网络算子分系统和EGA分系统设计方法,解决了多准则目标优化的NP完全问题,并对模型的预测效果进行比较分析。采集美国27年间(1990-2016年)军费开支时间序列进行实证检验,分析结论认为非线性灰色神经网络算子能够有效提高模型精度,EGA算法在收敛速度与精度上优于标准遗传算法,采用所建立的预测模型进行军费开支预测精度更高,效果更好。

Elite genetic improved nonlinear gray neural network operator and military expenditure multi-objective combination forecasting application

Military expenditure is a kind of nonlinear time series with macroeconomic characteristics under the complex economic system. Under the background of the study of military expenditure multi-objective combination forecasting, a nonlinear grey neural network metering combined forecasting model based on the elite genetic algorithm (EGA) is proposed. The overall modeling ideas and nonlinear grey neural network operator sub-system and EGA sub-system design methods are given. The NP-complete problem of multi-objective optimization is solved, and the forecasting effect of the model is analysed. Collecting the United States 27 (1990-2016) years of military expenditure data for empirical testing, the result shows that the nonlinear gray neural network operator can improve the accuracy of the model effectively. The EGA is superior to the SGA in the convergence rate and convergence accuracy. The forecasting model which is used to forecast the military expenditure can get higher accuracy and better results.