设计多创新层次分数自适应算法,用于使用关键术语分离原理的通用双线参数系统
Yancheng Zhu1,2,3, Huaiyu Wu4,5, Zhihuan Chen1,3
1Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China.
Scientific reports
|December 31, 2024
概括
这项研究引入了新的算法,用于分数参数识别在通用双线参数 (GBIP) 系统与有色噪声. 多创新层次分数最小平均平方 (K-MHFLMS) 算法显示了比K-HFLMS方法更快的趋同.
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:
- 通用双线参数 (GBIP) 系统在各种工程领域普遍存在.
- 准确的参数识别对于有效控制和分析这些系统至关重要.
- 分数计算为复杂的动态系统提供了更现实的建模方法.
研究的目的:
- 为了应对GBIP系统中被彩色噪声损坏的分数参数识别的挑战.
- 开发和评估先进的自适应过算法,以改善系统识别.
- 在准确性和趋同速度方面,将新型算法的性能与现有方法进行比较.
主要方法:
- 基于关键术语分离原理的层次分数最小平均平方 (K-HFLMS) 算法的开发.
- 引入多创新层次分数最小平均平方 (K-MHFLMS) 算法,增强数据利用.
- 使用健身指标,平均平方误差和平均预测输出误差进行比较性能分析.
主要成果:
- 该K-MHFLMS算法扩展了尺度创新向向量创新,利用更多的系统数据.
- 模拟结果证明了K-HFLMS和K-MHFLMS在不同噪声条件,分数订单和创新长度下的有效性和可靠性.
- 与K-HFLMS相比,K-MHFLMS的融合率更快,特别是随着创新时间的增加.
结论:
- 无论是K-HFLMS还是K-MHFLMS,都有效用于用彩色噪声识别GBIP系统的分数参数识别.
- 在融合速度方面,K-MHFLMS算法提供了卓越的性能,使其成为系统识别的宝贵工具.
- 该研究通过严格的模拟验证了拟议的算法,证实了它们的实际适用性.
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