系统工程与电子技术

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基于合成灰数灰度的区间灰数自忆性预测模型

郭晓君1, 2,刘思峰1,方志耕1   

  1. 1. 南京航空航天大学经济与管理学院, 江苏 南京 211106;
    2. 南通大学理学院, 江苏 南通 226007
  • 出版日期:2014-06-16 发布日期:2010-01-03

Self-memory prediction model of interval grey number based on grey degree of compound grey number

GUO Xiao-jun1, 2, LIU Si-feng1, FANG Zhi-geng1   

  1. 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. School of Science, Nantong University, Nantong 226007, China
  • Online:2014-06-16 Published:2010-01-03

摘要:

针对传统灰色预测模型解决灰数序列预测的局限性,为了提高预测精度,以考虑合成灰数灰度的区间灰数预测模型为基础,构建了基于合成灰数灰度的区间灰数自忆性耦合预测模型,结合动力系统自忆性原理克服了传统灰色预测模型对初值比较敏感的弱点。算例仿真以具饱和发展状态特征的区间灰数序列为对象,获得了满意的模拟预测精度,验证了所构建模型的有效性及优越性。研究结果表明,本文提出的新模型丰富和完善了区间灰数预测模型体系,并拓展了其应用范围。

Abstract:

In view of the limitation of traditional grey prediction models for forecasting grey number sequence, the interval grey number self memory coupling prediction model is established based on the grey degree of compound grey number in order to improve the prediction accuracy. This model is based on the grey degree of compound grey number by combining the self memory principle of dynamic system, by which the traditional grey prediction model’s weakness of being sensitive to initial value can been overcomed. The simulation example takes an interval grey number sequence featuring saturated development as the object, and demonstrates the effectiveness and superiority of the proposed model which can achieve satisfactory accuracy of fitting and forecasting. The research results indicate that the proposed model can enrich and perfect the interval grey number prediction model system, as well as extend its application span.