基于TCN-LSTM网络的多变量系统预测,具有自我注意机制和LASSO变量选择
Yiqin Shao1, Jiale Tang2,3, Jun Liu2,3
1Key Laboratory of Intelligent Textile and Flexible Interconnection of Zhejiang Province,College of Textiles Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
ACS omega
|December 25, 2023
概括
本研究引入了一种具有自我注意机制的新型TCN-LSTM网络,用于准确预测复杂系统输出. 该模型有效地识别了关键变量,提高了对 CO 含量监测等工业应用的预测准确性.
科学领域:
- 人工智能的人工智能
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 在复杂的工业系统中,准确预测难以测量的输出变量至关重要.
- 现有的模型经常在多变量数据和特征冗余性方面扎.
研究的目的:
- 为多变量特征序列开发一种智能动态建模方法.
- 提高复杂系统中预测关键输出变量的准确性和效率.
主要方法:
- 使用最少绝对收缩和选择运算符 (LASSO) 来选择主要输入变量.
- 设计了一个与自我注意机制 (SAM) 融合的时间卷积网络-LSTM (TCN-LSTM).
- TCN提取输入特征,LSTM处理时间序列内存,SAM优化特征加权.
主要成果:
- 拟议的TCN-LSTM-SAM模型与现有网络模型相比,表现出优越的性能.
- 使用来自合成氨行业转换器的离线数据成功预测了CO含量.
- 拉索算法有效地减少了网络冗余和计算负载.
结论:
- 在复杂的工业过程中,TCN-LSTM-SAM模型为动态建模和预测提供了强大而准确的解决方案.
- 整合LASSO,TCN,LSTM和SAM显著提高了预测准确性和模型效率.
- 该模型的适用性已在合成氨行业中得到验证,用于CO含量预测.
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