基于神经模糊网络的非线性混合主动噪声控制系统
Thi Trung Tin Nguyen1, Jing Na1, Le Thai Nguyen2
1Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了用于混合动力主动噪声控制 (HANC) 系统的自适应神经模糊网络 (ANFN) 控制器. 这种新方法在现实应用中提高了噪声抑制的有效性和稳定性.
科学领域:
- 声学和信号处理
- 控制系统工程 控制系统工程
- 在工程领域的人工智能.
背景情况:
- 积极噪音控制 (ANC) 对于减轻环境噪音污染至关重要.
- 现有的ANC系统在复杂环境中面临着强度和手动微调方面的挑战.
- 混合动力主动噪声控制 (HANC) 集成了多种策略,以提高性能.
研究的目的:
- 开发一种新的自适应神经模糊网络 (ANFN) 控制器,用于增强混合动力主动噪声控制 (HANC).
- 提高主动噪声抑制的稳定性和有效性.
- 解决非线性问题,减少复杂声学环境中的手动调整.
主要方法:
- 一个自适应的神经网络被设计来最大限度地降低余噪的平均平方误差.
- 一个模糊的逻辑策略被纳入处理环境非线性和减少手动调.
- 拟议的基于ANFN的HANC控制器的稳定性使用Lyapunov定理被严格证明.
主要成果:
- 数字模拟证明了拟议的基于ANFN的HANC方法的有效性.
- 与现有方法相比,控制器在主动降噪方面表现优越.
- 该系统在各种具有挑战性的噪声信号条件下证明了其稳健性.
结论:
- 拟议的自适应性神经模糊网络控制器显著提高了混合动力主动噪声控制性能.
- 这种基于ANFN的方法为现实世界噪音污染抑制提供了强大而有效的解决方案.
- 该方法成功地克服了复杂环境中传统ANC系统的局限性.
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