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Neuro-Fuzzy Network-Based Nonlinear Hybrid Active Noise Control Systems.

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

Summary

This summary is machine-generated.

This study introduces an adaptive neuro-fuzzy network (ANFN) controller for hybrid active noise control (HANC) systems. The new method enhances noise suppression effectiveness and robustness in real-world applications.

Keywords:

active noise control hybrid active noise control neural network nonlinear filter

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Area of Science:

Acoustics and Signal Processing
Control Systems Engineering
Artificial Intelligence in Engineering

Background:

Active noise control (ANC) is crucial for mitigating environmental sound pollution.
Existing ANC systems face challenges with robustness and manual fine-tuning in complex environments.
Hybrid active noise control (HANC) integrates multiple strategies for improved performance.

Purpose of the Study:

To develop a novel adaptive neuro-fuzzy network (ANFN) controller for enhanced hybrid active noise control (HANC).
To improve the robustness and effectiveness of active noise suppression.
To address nonlinearities and reduce manual adjustments in complex acoustic environments.

Main Methods:

An adaptive neural network was designed to minimize the mean square error of residual noise.
A fuzzy logic strategy was incorporated to handle environmental nonlinearities and reduce manual tuning.
The stability of the proposed ANFN-based HANC controller was rigorously proven using Lyapunov theorem.

Main Results:

Numerical simulations demonstrated the effectiveness of the proposed ANFN-based HANC method.
The controller showed superior performance in active noise suppression compared to existing approaches.
The system proved robust under various challenging noise signal conditions.

Conclusions:

The proposed adaptive neuro-fuzzy network controller significantly enhances hybrid active noise control performance.
This ANFN-based approach offers a robust and effective solution for real-world noise pollution suppression.
The method successfully overcomes limitations of traditional ANC systems in complex environments.