基于自我错误学习框架的算法用于扩展的维纳-哈默斯坦系统的参数恢复,这些系统接受了量化测量
Haozhe Cao1, Lihua Li1, Yunduo Feng2
1School of National Security, People's Public Security University of China, Beijing 100038, PR China.
ISA transactions
|May 15, 2024
概括
这项研究引入了一种新的方法,用于估计具有量化数据的复杂非线性系统中的参数. 新的自我错误学习框架提高了系统识别的准确性和趋同性.
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:
- 准确的系统识别对于控制和分析至关重要.
- 歇斯底里非线性和量子化测量带来了重大挑战.
- 现有的估计算法在复杂的场景中可能缺乏性能.
研究的目的:
- 为扩展的维纳 - 汉默斯坦系统提出一个新的估计方案.
- 为解决在hysteresis非线性和量化测量下的参数识别.
- 与传统方法相比,提高估计性能.
主要方法:
- 开发一个自我错误学习框架.
- 引入适应性波器,从受污染的信号中提取数据.
- 使用过数据推导识别错误表达式.
- 关于在线补偿估计错误变量的建议.
- 设计一个新的自适应定律,具有自适应递归增益.
主要成果:
- 从噪音测量中有效提取有用的识别数据.
- 消除回归向量的效应对收性能的影响.
- 在线验证回归者的持续激发 (PE) 状态.
- 在一般PE条件下严格证明估计器的趋同.
- 通过两个例子和一个现实世界的案例来证明有效性.
结论:
- 拟议的估计方案为复杂系统提供了高性能参数识别.
- 自错学习框架有效地处理hysteresis非线性和量化数据.
- 该方法显示了与经典估计算法相比的显著改进.
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