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Updated: Jul 2, 2025

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研究一种新的多变量灰色模型及其在二氧化碳排放预测中的应用
Environmental science and pollution research international
|February 24, 2024
概括
一个新的分数多变量灰色模型 (FBNGM) 提高了对二氧化碳排放的小样本预测精度. 这种先进的模型利用情报优化进行卓越的预测,帮助制定政策.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 数学建模的数学建模
背景情况:
- 精确的小样本预测具有挑战性,特别是在数据有限的发展中国家.
- 灰色模型对于小样本预测是有效的,但对于复杂的场景需要改进.
- 预测二氧化碳排放对环境政策至关重要,需要强大的建模技术.
研究的目的:
- 提出一个新的分数多变量灰色模型,FBNGM (1,N,r),用于增强小样本预测.
- 通过结合分数顺序运算符和智能优化算法来提高预测准确性.
- 评估模型在预测二氧化碳排放和确定影响因素方面的有效性.
主要方法:
- 使用分数订单运算符开发FBNGM (1,N,r) 模型.
- 智能优化算法的应用用于参数调整.
- 使用二氧化碳排放数据来评估模型性能的数值实验.
主要成果:
- 与现有模型相比,FBNGM (1,N,r) 模型显示出更高的预测准确性.
- 该模型有效地利用了所有可用的数据,避免了过拟合问题.
- 通过模型的分析,证实了对二氧化碳排放的关键影响因素.
结论:
- 该FBNGM (1,N,r) 模型为CO2排放的小样本预测准确性提供了显著的进步.
- 该模型预测未来排放的能力为政策制定提供了宝贵的见解.
- 分数顺序运算符和情报优化提高了多变量灰色模型的性能.
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