基于初始价值优化的维纳 - 汉默斯坦系统的递归层次参数识别.
Qiangya Li1, Tao Liu1, Jing Na2
1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
ISA transactions
|January 27, 2025
概括
一种新的递归方法准确地识别了与噪声有关的维纳-哈默斯坦系统中的参数. 这种方法优化了使用粒子群优化 (PSO) 的初始值,以改进系统识别.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 系统识别系统识别系统
背景情况:
- 维纳-哈默斯坦系统被广泛使用,但由于其非线性结构和随机噪声,很难识别.
- 传统的递归识别方法经常受到初始值灵敏度和参数交叉合的影响.
研究的目的:
- 为维纳-哈默斯坦系统提出一种新的递归层次参数识别方法.
- 解决系统识别中随机噪声,参数交叉合和初始值灵敏性的挑战.
主要方法:
- 一个针对唯一参数表达的Wiener-Hammerstein模型的概括式.
- 一个分层的识别算法,将合和未合的参数分开.
- 一个辅助块模型用于预测内部变量并确保一致的估计.
- 适应性遗忘因素,以提高趋同率.
- 粒子集群优化 (PSO) 用于初始值的优化.
主要成果:
- 拟议的方法成功地识别了具有随机噪声的维纳 - 汉默斯坦系统中的参数.
- 层次识别有效地避免了参数交叉合.
- 基于PSO的初始价值优化减轻了敏感性问题.
- 微定位阶段的实验验证证证了该方法的有效性.
结论:
- 新的递归层次方法为维纳 - 汉默斯坦系统提供了准确而强大的参数识别.
- 这种方法增强了趋同,克服了传统方法的局限性.
- 经过验证的方法显示了系统控制和分析中的实际应用的巨大潜力.
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