基于遗传算法的GM(1,1)模型最优α水平设置

IF 1 4区 工程技术 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Kuo-Chen Hung, Chia-Yi Chien, Kuo-Jung Wu, Fu-Yuan Hsu
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引用次数: 18

摘要

灰色预测模型GM(1,1)具有数据量最少的特点,已成功应用于各个领域。然而,已经发现在预测操作中不同的α水平可能直接引起不同的误差。因此,参数α在预测中起着重要的作用。因此,如何寻找参数α的最优设置是一项有价值的工作。本文将遗传算法(GA)方法应用于GM(1,1)模型来处理这一问题。我们提出了两个说明性的例子来比较邓的方法和我们修正的方法。这些结果是有用的,因为它们缩小了误差范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Alpha Level Setting in GM (1, 1) Model Based on Genetic Algorithm
The grey forecasting model, GM (1, 1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, it has been discovered that different errors may be directly induced by different alpha levels in predicted operations. Accordingly, the parameter α plays an important role on forecasting. Thus, how to search for the optimal setting of parameter α is a valuable work. In this paper, Genetic Algorithm (GA) method has be applied in GM (1, 1) model for handling this problem. We present two illustrative examples to compare between Deng's method and our revised method. These results are useful in that they diminish the margin of error.
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来源期刊
Journal of Grey System
Journal of Grey System 数学-数学跨学科应用
CiteScore
2.40
自引率
43.80%
发文量
0
审稿时长
1.5 months
期刊介绍: The journal is a forum of the highest professional quality for both scientists and practitioners to exchange ideas and publish new discoveries on a vast array of topics and issues in grey system. It aims to bring forth anything from either innovative to known theories or practical applications in grey system. It provides everyone opportunities to present, criticize, and discuss their findings and ideas with others. A number of areas of particular interest (but not limited) are listed as follows: Grey mathematics- Generator of Grey Sequences- Grey Incidence Analysis Models- Grey Clustering Evaluation Models- Grey Prediction Models- Grey Decision Making Models- Grey Programming Models- Grey Input and Output Models- Grey Control- Grey Game- Practical Applications.
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