基于智能梯形和可变重量组合的重建转基因模型.
Shanhua Zhang1, Hong Ki An2, Hongmei Yin1
1Department of Digital Equipment, Jiangsu Vocational College of Electronics and Information, Huai'an, 223003, China.
Heliyon
|September 4, 2024
概括
这项研究通过使用智能梯形和可变重量方法重建背景值来提高灰色模型 (GM(1,1) 预测准确度. 这些新模型改善了指数增长和流量量数据的预测.
科学领域:
- 数学建模的数学建模
- 时间序列分析时间序列分析.
- 预测分析是一种预测分析.
背景情况:
- 灰色模型 (GM(1,1)) 预测准确性对背景值估计非常敏感.
- 传统的梯形背景值对特定数据序列的适用性有限.
研究的目的:
- 提出一个改进的GM(1,1) 背景值重建方法.
- 为了提高GM模型的适用性和预测准确性.
主要方法:
- 开发的模型I:带有可调节参数的形背景值函数.
- 开发的模型II:使用参数序列的新型背景值函数.
- 采用遗传算法来优化两个模型的参数.
主要成果:
- 与传统方法相比,模型I和模型II对指数增长数据的预测准确度更高.
- 模型I比道路交通量数据的现有模型提高了0.3643%和0.2725%的预测准确度.
- 与模型I相比,模型II进一步提高了0.1075%的预测准确性.
结论:
- 拟议的智能梯形和可变重量方法显著改善了GM的背景值估计.
- 改进的GM(1,1) 模型为时间序列预测提供了更高的准确性和更广泛的适用性.
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