一个新的SVD-UKFNN算法用于预测电解的当前效率
Xiaoyan Fang1, Xihong Fei2, Kang Wang3
1School of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Scientific reports
|March 18, 2025
概括
一种新方法改善了电解生产中的当前效率预测. 新的单一价值分解无香卡尔曼过神经网络 (NSVD-UKFNN) 提高了工业应用的准确性和稳定性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 电解生产工艺 (AEPP) 的效率对工业产出至关重要.
- 在AEPP的动态非线性和复杂性挑战准确的预测模型.
研究的目的:
- 开发一个先进的模型来预测AEPP当前效率.
- 在复杂的工业系统中提高预测准确性和稳定性.
主要方法:
- 使用人工神经网络 (ANN) 构建了一个动态预测模型,在一个未经过的卡尔曼过神经网络 (UKFNN) 中.
- 集成的单数值分解 (SVD) 与UKFNN用于提高数值稳定性.
- 重新定义预测方差作为成本函数,通过梯度下降优化,以减少错误积累.
主要成果:
- 拟议的NSVD-UKFNN显著提高了预测准确度.
- 实现了平均绝对误差 (MAE) 的2.08倍减少.
- 与基线模型相比,平方误差 (SSE) 的总和减少了大约22.23倍.
结论:
- NSVD-UKFNN为预测AEPP当前效率提供了强大的和准确的解决方案.
- 这种方法解决了AEPP固有的复杂性所带来的挑战.
- 这些发现支持在电解中提高工业生产效率和质量.
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