基于随机里叶特征的非线性循环内核规范化的LMS算法,具有多个反
Ji Zhao1, Jiaming Liu1, Qiang Li1
1School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010, PR China.
ISA transactions
|November 12, 2024
概括
本研究引入了具有多个反的非线性反复内核自适应过 (NR-KNLMS-MF),以提高性能. 还提出并验证了一种使用随机里埃特征 (RFF-NR-KNLMS-MF) 的高效变体.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 具有非线性反复结构的内核自适应过算法 (KAF) 由于非线性反,比传统的KAF表现更好.
- 现有的非线性循环 KAF 通过专注于单个反输出而受到限制,这可能会限制它们的过能力.
研究的目的:
- 引入一种使用多个反输出的新型非线性循环内核自适应过算法.
- 开发这种算法的高效变体,以解决计算复杂性的问题.
- 从理论上分析收性质,并从经验上验证拟议的方法.
主要方法:
- 开发非线性循环内核规范最小平均平方与多重反 (NR-KNLMS-MF) 算法.
- 整合随机里埃特征 (RFF) 来创建一个高效的版本,RFF-NR-KNLMS-MF.
- 对RFF-NR-KNLMS-MF的平均平方收的理论分析.
- 通过对时间序列预测任务的模拟来评估性能.
主要成果:
- 拟议的NR-KNLMS-MF算法通过利用来自多个反输出的信息来证明卓越的性能.
- RFF-NR-KNLMS-MF变体有效地减少了计算复杂性,同时保持了高性能.
- 与现有方法相比,模拟结果证实了两种拟议的算法的有效性.
结论:
- 新的NR-KNLMS-MF算法通过多重反利用提高了过能力.
- RFF-NR-KNLMS-MF为复杂的信号处理任务提供了高效和有效的解决方案.
- 拟议的算法代表了非线性循环内核自适应过的重大进步.
相关概念视频
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