利用未标记的数据:基于BiLSTM-Deep自编码器的增强稀土成分含量预测
Wenhao Dai1, Rongxiu Lu1, Jianyong Zhu1
1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China; Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang, 330013, Jiangxi, China.
ISA transactions
|January 5, 2025
概括
这项研究引入了一种新的BiLSTM-Deep自编码器增强的LSSVM模型,用于稀土成分预测. 它有效地利用未标记的数据来显著提高预测准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 对于稀土成分预测的传统监督学习模型面临挑战,原因是有限的标记数据和大量未标记数据的不足利用.
- 现有的方法在与稀土生产过程中固有的数据稀疏性和时间依赖性作斗争.
- 精确预测稀土成分含量对于优化开采和炼油过程至关重要.
研究的目的:
- 开发一种先进的预测模型,利用未标记的数据来提高稀土成分含量预测的准确性.
- 在稀土元素分析中克服传统监督学习方法的局限性.
- 提出一种新的方法,将深度学习与传统机器学习相结合,以提高预测性能.
主要方法:
- 开发了一个BiLSTM-Deep自编码器,用于从稀土生产数据中进行无监督的特征提取,捕获时间序列特征.
- 在Deep自编码器中使用了布尔向量来模拟噪音和缺失的数据,从而提高了特征提取的稳定性.
- 提取的隐性特征与最小平方支持向量机 (LSSVM) 算法融合,创建了BiLSTM-DeepAE-LSSVM预测模型.
主要成果:
- 与传统方法相比,拟议的BiLSTM-DeepAE-LSSVM模型在预测稀土成分含量方面表现优越.
- 这种方法有效地利用了大量未标记的数据,与仅监督技术相比,这是一个显著的改进.
- 使用LaCe/PrNd提取现场数据的模拟结果验证了模型的准确性和稳定性.
结论:
- 新的BiLSTM-DeepAE-LSSVM方法成功地利用来自稀土开采过程的未标记数据来提高预测准确度.
- 这种方法在稀土成分分析中比传统的监督学习模型提供了显著的进步.
- 这些发现突出了将深度自动编码器与LSSVM集成到复杂工业数据预测中的潜力.
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