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在非高斯噪音下,一个强大的复杂α-西格亲缘投影算法
Yaowei Guo1, Bin Guo1, Guobing Qian1
1College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了一种使用α-Sigmoid成本函数 (α-CSAP) 的新型复杂值自适应过算法. α-CSAP算法通过抑制干扰和减少复杂性来提高噪音环境中的性能.
科学领域:
- 信号处理 信号处理
- 适应性过是一种自适应性过.
- 计算智能是一种计算智能.
背景情况:
- 传统的自适应过算法在相关信号和非高斯噪声下遭受性能退化.
- 冲动噪声和矩阵反转增加了现有方法的计算复杂性.
研究的目的:
- 为具有挑战性的信号环境提出一个强大的复杂值自适应选算法.
- 为了减少计算复杂性,同时保持适应性系统的高性能.
主要方法:
- 开发一个包含α-Sigmoid成本函数 (α-CSAP) 的复杂值亲缘投影算法.
- 隐含的可变阶段大小更新通过正常化因子来抑制冲动噪声.
- 稳定状态平均平方偏差 (MSD) 的理论推导.
主要成果:
- α-CSAP算法有效地抑制了冲动性噪声干扰.
- 拟议的方法避免了矩阵反转,从而降低了计算复杂性.
- 与传统算法相比,在系统识别和光束成形方面表现出卓越的性能.
结论:
- α-CSAP算法为复杂的自适应过提供了更高的稳定性和效率.
- 这种新的方法解决了现有的适应性过技术在实际场景中的关键局限性.
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