扩展三明治模型的自适应参数估计
Guanglu Yang1,2, Huanlong Zhang3, Yubao Liu4
1Nanyang Cigarette Factory of Henan China Tobacco Industry Co., Ltd, Nanyang, 473000, People's Republic of China.
Scientific reports
|June 16, 2023
概括
本研究介绍了扩展三明治系统的新递归识别算法. 这种新的方法使用参数识别错误数据,提高了系统识别准确度,为算法设计提供了新的视角.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统识别 非线性系统识别
- 信号处理 信号处理
背景情况:
- 扩展三明治系统是先进的非线性块导向模型,对于描述复杂的工业过程至关重要.
- 传统的系统识别方法通常依赖于预测错误输出,限制了它们的适用性.
- 最近的研究强调了对这些系统更强大的识别技术的需要.
研究的目的:
- 为扩展三明治系统提出一种新的递归识别算法.
- 根据参数识别错误数据开发一个自适应估计器.
- 为系统识别算法提供一个新的设计框架.
主要方法:
- 使用参数识别错误数据开发了一个递归识别算法.
- 使用过器以极简的结构提取系统信息.
- 中间变量是使用过的矢量来设计的,以获得识别错误数据.
- 通过整合衍生的识别错误数据来建立自适应估计器.
主要成果:
- 拟议的算法在一般的连续激发条件下趋于真参数值.
- 实验结果证明了新型识别方法的有效性和实用性.
- 新方法为传统的基于预测错误的估计器提供了有价值的替代方案.
结论:
- 开发的递归识别算法为扩展的三明治系统提供了一种新且有效的方法.
- 该方法依赖参数识别错误数据,为算法设计提供了新的视角.
- 该研究通过模拟和实验数据验证了算法的性能,证实了其实际适用性.
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