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微弧氧化过程的数学模型的参数识别
Anatoliy Semenov1, Ekaterina Pecherskaya1, Pavel Golubkov1
1Department of Information and Measurement Equipment and Metrology, Penza State University, Krasnaya Street 40, Penza 440026, Russia.
Heliyon
|October 9, 2023
概括
本研究介绍了一种神经网络算法,用于识别非线性模型中的参数,特别是微弧氧化. 该方法实现了高精度,电流和电压的标准偏差在±4%以内.
科学领域:
- 材料科学与工程 材料科学与工程
- 人工智能和机器学习
背景情况:
- 非线性模型的参数识别对于流程优化至关重要.
- 微弧氧化 (MAO) 是一种需要精确建模的表面处理过程.
研究的目的:
- 开发和验证一种新的算法,用于对非线性物体模型的参数识别.
- 将这个算法应用于微弧氧化过程的具体情况.
主要方法:
- 使用微弧氧化数据进行训练和控制样本的实验方法.
- 神经网络的顺序训练,以估计非线性模型的参数.
- 通过实验测试验证识别方法的验证.
主要成果:
- 提出的神经网络参数识别方法的准确性很高.
- 电流和电压与名义值的标准偏差在±4%以内.
- 结果支持对MAO的智能系统的开发.
结论:
- 开发的算法有效地执行非线性模型的参数识别.
- 该方法适用于现实世界中的应用,例如微弧氧化.
- 这项工作为保护性涂层生产的先进控制系统做出了贡献.
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