动态神经网络切换用于非线性系统的主动控制.
1Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, United Kingdom.
The Journal of the Acoustical Society of America
|July 7, 2025
概括
本研究介绍了一种用于主动噪音和振动控制的新型交换神经网络 (NN) 策略. 这种方法通过与传统方法相比,高效地管理计算负载,提高了非线性系统的性能.
科学领域:
- 控制工程 控制工程 控制工程
- 应用数学 应用数学 应用数学
- 信号处理 信号处理
背景情况:
- 传统的前式主动噪声和振动控制 (ANC/AVC) 系统通常使用线性数字过器,例如过的参考最小平均平方 (LMS) 算法.
- 线性控制器很难准确地建模和控制表现出非线性动态的系统,导致性能不佳.
- 神经网络 (NN) 控制器显示出对非线性控制的承诺,但可能是计算密集型,限制其实时应用.
研究的目的:
- 为非线性系统中主动噪声和振动控制制定一个计算效率高的控制策略.
- 在一系列非线性系统行为中保持高控制性能.
- 在动态环境中解决与传统神经网络控制器相关的计算费用.
主要方法:
- 建议采用动态切换神经网络 (NN) 的新型控制策略.
- 个别的NN被训练为非线性系统的特定操作范围.
- 该系统在这些较小的,专门的NN之间动态切换,以适应不同的非线性.
主要成果:
- 建议的切换NN方法与单个,更大的通用NN控制器相比,显示出更高的控制性能.
- 与基于功能链接人工神经网络 (FLANN) 的控制器相比,也观察到性能优势.
- 模拟包括一个具有非线性刚度的系统,以及对物理非线性动态系统的离线测试.
结论:
- 动态切换较小的,专业的NN之间是改善非线性系统中主动噪声和振动控制的有效策略.
- 这种方法为在非线性动态环境中基于NN控制的计算挑战提供了实际的解决方案.
- 拟议的方法为非线性应用提供了相对于现有的NN和线性控制技术的显著性能优势.
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