自学型-2模糊系统与适应性规则减少用于时间序列预测
Abdulwhab Alkharashi1, Gaganjot Kaur2, Hadeel Alsolai3
1Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Jeddah, Saudi Arabia.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种自学类型-2模糊系统,可以有效处理时间序列预测中的不确定性. 它优化规则以提高准确性和计算效率,优于现有模型.
科学领域:
- 人工智能的人工智能
- 计算智能是一种计算智能.
- 时间序列分析时间序列分析
背景情况:
- 不确定性和混乱的振荡阻碍了时间序列预测.
- 1型模糊系统在很高的不确定性下扎.
- 2型模糊系统提供更好的不确定性处理,但可能过于复杂.
研究的目的:
- 开发一种自学型-2模糊系统 (SLT2FS),用于增强时间序列预测.
- 提高2型模糊模型的解释性和计算效率.
- 为在动态环境中在线部署提供可扩展的解决方案.
主要方法:
- 结合了参与式学习 (PL) 和内核递归最小方程 (KRLS) 在线学习.
- 采用适应性减少规则策略,以消除冗余规则.
- 使用基于Type-2模糊集的兼容性测量来考虑不确定性.
主要成果:
- 在复杂的数据集上表现出卓越的预测性能,如Mackey-Glass混乱时间序列和TAIEX.
- 与最先进的模型相比,实现较低的错误指标,规则基础显著减少.
- 通过自适应规则优化保持高精度和计算效率.
结论:
- 拟议的SLT2FS与自适应规则减少是一种可扩展和高效的方法,用于在不确定性下准确的时间序列预测.
- 该模型能够保持小规则基础,同时优化性能,使其适合实时应用.
- 该方法为金融市场,工业流程和其他需要在动态,不确定的环境中精确预测的领域提供了强大的解决方案.
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