標題: A Novel Mutual Fractional Grey Bernoulli Model With Differential Evolution Algorithm and Its Application in Education Investment Forecasting in China
作者: Xie, Wanli
Pu, Bin
Pei, Chunying
Lee, Shin-Jye
Kang, Yan
科技管理研究所
Institute of Management of Technology
關鍵字: Predictive models;Mathematical model;Investment;Education;Forecasting;Indexes;Optimization;Grey system;grey forecasting model;non-linear grey bernoulli model;fractional order;differential evolution algorithm;educational investment
公開日期: 1-Jan-2020
摘要: 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 (<italic>i.e.</italic>, 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 & x2019;s education investment in 2020 & x2013;2025.
URI: http://dx.doi.org/10.1109/ACCESS.2020.2995974
http://hdl.handle.net/11536/154926
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2995974
期刊: IEEE ACCESS
Volume: 8
起始頁: 97839
結束頁: 97850
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