基于模糊推理的LSTM用于长期时间序列预测
Weina Wang1, Jiapeng Shao2, Huxidan Jumahong3
1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China. wangweina@jlict.edu.cn.
Scientific reports
|November 22, 2023
概括
本研究引入了一个基于模糊推理的长期短期记忆 (LSTM) 网络,以改善长期时间序列预测. 这种新的方法通过将模糊逻辑集成到LSTM架构中来提高预测准确性和可解释性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 长短期记忆 (LSTM) 网络在时间序列预测方面面临挑战,包括累积错误,时间相关性减少和解释性差.
- 这些局限性阻碍了LSTM模型的长期预测性能.
研究的目的:
- 提高LSTM的准确性和可解释性,用于长期时间序列预测.
- 解决传统LSTM模型固有的局限性.
主要方法:
- 提出了一个基于模糊推理的新型LSTM,将模糊系统集成到LSTM网络中.
- 基于Wang-Mendel (WM) 的快速和完整的模糊规则构建方法被引入,用于高效的模糊规则简化和补充.
- 关键组件包括模糊预测融合,强化记忆层和参数分割共享策略.
主要成果:
- 拟议的基于模糊推理的LSTM显示了与现有模型相比更好的预测性能.
- 该方法有效地提高了LSTM网络的推理能力和可解释性.
- 长期记忆力得到加强,渐变分散问题得到缓解.
结论:
- 基于模糊推理的LSTM为长期时间序列预测提供了一种优越的方法.
- 模糊逻辑的集成显著提高了准确性和可解释性.
- 该模型为复杂的时间序列预测任务提供了更强大,更易于理解的解决方案.
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