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相关概念视频

Active Filters01:25

Active Filters

793
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
793
Passive Filters01:27

Passive Filters

523
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
523
Generator Voltage Control01:21

Generator Voltage Control

132
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
132
Feedback control systems01:26

Feedback control systems

295
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
295
Effects of feedback01:24

Effects of feedback

527
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
527
Open and closed-loop control systems01:17

Open and closed-loop control systems

678
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
678

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相关实验视频

Updated: Jun 12, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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GFANC-RL:基于强化学习的生成式固定过器主动噪声控制

Zhengding Luo1, Haozhe Ma2, Dongyuan Shi1

  • 1Digital Signal Processing Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Neural networks : the official journal of the International Neural Network Society
|September 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了强化学习 (GFANC-RL) 的生成式固定过器主动噪声控制,消除了手动数据标签,以改善降噪. 这种新的方法提高了卷积神经网络 (CNN) 的准确性和系统性能.

关键词:
活动噪音控制 活动噪音控制卷积神经网络是一个卷积神经网络.发电式固定过器ANC 连接器强化学习是一种强化学习.

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科学领域:

  • 声学 声学 在声学上
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 生成式固定过器主动噪声控制 (GFANC) 提供了降噪与稳定之间的平衡.
  • 训练GFANC的卷积神经网络 (CNN) 需要广泛的,准确标记的噪音数据,这是资源密集的,容易出现错误.
  • 标签不准确可能会严重损害GFANC系统中CNN的过器生成精度.

研究的目的:

  • 提出一种新的基于强化学习的GFANC (GFANC-RL) 方法.
  • 消除在GFANC系统中需要手动标记噪声数据的需要.
  • 通过自动学习来提高GFANC方法的准确性和效率.

主要方法:

  • 开发了一个GFANC-RL框架,利用强化学习 (RL) 来自动化CNN参数更新.
  • 采用RL的探索能力来绕过数据标签过程.
  • 使用RL算法解决了与GFANC中的二进制组合权重相关的非可区分性挑战.

主要成果:

  • 模拟结果证实了GFANC-RL方法的有效性.
  • 证明了GFANC-RL在处理现实世界记录的噪音中的可转移性.
  • 在多种不同的声学路径中验证了性能,展示了强度.

结论:

  • 在GFANC培训中,GFANC-RL成功地消除了对标记数据的依赖.
  • 拟议的方法实现了精确的降噪,并保持了系统的稳定性.
  • 对于主动噪声控制应用,GFANC-RL提供了一个更高效,更强大的解决方案.