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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

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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...
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Related Experiment Video

Updated: Jun 12, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

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: Reinforcement Learning-based Generative Fixed-filter Active Noise Control.

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
Summary
This summary is machine-generated.

This study introduces Generative Fixed-filter Active Noise Control with Reinforcement Learning (GFANC-RL), eliminating manual data labeling for improved noise reduction. The novel approach enhances Convolutional Neural Network (CNN) accuracy and system performance.

Keywords:
Active Noise ControlConvolutional Neural NetworkGenerative Fixed-filter ANCReinforcement Learning

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Generative Fixed-filter Active Noise Control (GFANC) offers a balance between noise reduction and stability.
  • Training GFANC's Convolutional Neural Network (CNN) requires extensive, accurately labeled noise data, which is resource-intensive and prone to errors.
  • Labeling inaccuracies can significantly impair the CNN's filter-generation precision in GFANC systems.

Purpose of the Study:

  • To propose a novel Reinforcement Learning-based GFANC (GFANC-RL) approach.
  • To eliminate the need for manual noise data labeling in GFANC systems.
  • To enhance the accuracy and efficiency of the GFANC method through automated learning.

Main Methods:

  • Developed a GFANC-RL framework utilizing Reinforcement Learning (RL) to automate CNN parameter updates.
  • Employed the exploring capabilities of RL to bypass the data labeling process.
  • Addressed the non-differentiability challenge associated with binary combination weights in GFANC using RL algorithms.

Main Results:

  • Simulation results confirm the effectiveness of the GFANC-RL method.
  • Demonstrated the transferability of GFANC-RL in processing real-world recorded noises.
  • Validated performance across diverse acoustic paths, showcasing robustness.

Conclusions:

  • GFANC-RL successfully removes the dependency on labeled data for GFANC training.
  • The proposed method achieves accurate noise reduction and maintains system stability.
  • GFANC-RL offers a more efficient and robust solution for active noise control applications.