Abstract
Nowadays, the nonlinear grey Bernoulli model [NGBM(1,1)] has been successfully applied to various fields. Its main advantage is that the power exponent can better reflect the non-linear characteristics of the original data. However, the parameters of the model (i.e., the order of accumulation, coefficient of background value, and power index) must be optimized to fit the development law of the system. In this study, a fractional non-linear grey Bernoulli model [MFNGBM (1,1)] is proposed to reduce the perturbation limit of the classical NGBM and further improve the accuracy of the model, which uses mutual fractional operators and a new optimization scheme with a differential evolution (DE) algorithm for forecasting education investment. In the scheme, the power exponent of the Bernoulli differential equation, coefficient of background, and cumulative order of the original sequence are taken as decision variables, and their optimal parameters obtained by iteratively adjusting fitness functions. The experimental evaluation is conducted on two types of open-source data, and the results show that the proposed method can be very competitive with the popular baselines. Finally, MFNGBM(1,1) is used to predict China's education investment in 2020-2025.
Highlights
With rapid economic development and an aging population, the cultivation and building of a talent reserve have become one of the development strategies of various countries
Our approach achieves the best performance in X-sequence data prediction, which demonstrates the overall effectiveness of MFNGBM
2) RESULTS ON EDUCATION FUNDS IN CHINA The above chapter shows that the MFNGBM optimized by differential evolution (DE) is suitable for small sample modeling without additional data
Summary
With rapid economic development and an aging population, the cultivation and building of a talent reserve have become one of the development strategies of various countries. Achieving this goal largely relies on investment in education. Taking the allocation of educational resources in China as an example, as shown, one can find that China’s investment in educational resources is increasing year by year. The distribution of educational resources in southeast China has been far higher than that in northwest China. Forecasting education investment funds has profound guiding significance when making corresponding policies for the government and provides corresponding strategies for the government to allocate educational resources, which have practical application values
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