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Study of the generalized discrete grey polynomial model based on the quantum genetic algorithm

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成果类型:
期刊论文
作者:
Liu, Chong;Wu, Wen-Ze;Xie, Wanli
通讯作者:
Wen-Ze Wu
作者机构:
[Liu, Chong] Inner Mongolia Agr Univ, Sch Sci, Hohhot 010018, Peoples R China.
[Wu, Wen-Ze] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.
[Xie, Wanli] Nanjing Normal Univ, Inst EduInfo Sci & Engn, Nanjing 210097, Peoples R China.
通讯机构:
[Wen-Ze Wu] S
School of Economics and Business Administration, Central China Normal University, Wuhan, China
语种:
英文
关键词:
Discrete grey model;Quantum genetic algorithm (QGA);Prediction performance;Time power term;Fractional accumulation
期刊:
JOURNAL OF SUPERCOMPUTING
ISSN:
0920-8542
年:
2021
卷:
77
期:
10
页码:
11288-11309
基金类别:
Fundamental Research Funds for the Central Universities of ChinaFundamental Research Funds for the Central Universities [2019YBZZ062]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20 1144]
机构署名:
本校为其他机构
院系归属:
经济与工商管理学院
摘要:
The discrete grey model is increasingly used in various real-world forecasting problems, however, in the modeling procedure, neglecting the effect of the time power and requiring the integer-order accumulation impair the prediction performance to some extent. Considering this fact, this paper implements the fractional accumulating generation operator and time power term in the discrete grey polynomial model, and as a consequence, a generalized discrete grey polynomial model, namely GDGMP(1,1,N,α), is proposed. To further improve the prediction...

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