Study On A Grey Verhulst Self-Memory Model And Application

被引：0

作者：

Guo Xiaojun ^{[1
,2
]}

Liu Sifeng ^{[2
]}

Fang Zhigeng ^{[2
]}

机构：

[1] Nantong Univ, Sch Sci, Nantong 226007, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 211106, Peoples R China

来源：

PROCEEDINGS OF 2013 IEEE INTERNATIONAL CONFERENCE ON GREY SYSTEMS AND INTELLIGENT SERVICES (GSIS) | 2013年

基金：

中国国家自然科学基金;

关键词：

grey Verhulst model; fluctuant sigmoid process; grey Verhulst self-memory model; simulation and prediction precision;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

General grey Verhulst model for sigmoid process boasts good prediction accuracy. But the prediction accuracy is not high enough for fluctuant sigmoid process. In order to improve prediction accuracy, the grey Verhulst self-memory model was established based on grey system and self-memory principle. The model can recollect the historical data and get over grey Verhulst model's shortages. Examples show that the suggested model can reflect the fluctuant trend of sigmoid process with satisfactory simulation and prediction precision.

引用

页码：118 / 122

页数：5

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