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A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control.

Thi Trung Tin Nguyen¹, Faxiang Zhang¹, Jing Na¹

¹Yunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.

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|April 26, 2025

Summary

This summary is machine-generated.

A new adaptive neuro-fuzzy controller improves nonlinear active noise control (ANC) performance. This novel approach enhances noise suppression for complex environments using a variable step-size LMS algorithm.

Keywords:

active noise control adaptive neuro-fuzzy network filtered-x least-mean-square algorithm nonlinear path variable step-size learning

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Area of Science:

Signal Processing
Control Systems Engineering
Artificial Intelligence

Background:

Real-world environments present complex noise challenges for multi-sensor systems.
Existing active noise control (ANC) methods struggle with nonlinear noise sources.
Generative models and dynamic information fusion are key for advanced noise suppression.

Purpose of the Study:

To propose a novel adaptive neuro-fuzzy network controller for feedforward nonlinear ANC systems.
To enhance nonlinear noise suppression performance and system stability.
To address limitations of traditional ANC in complex acoustic environments.

Main Methods:

Developed a novel adaptive neuro-fuzzy network controller.
Implemented a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm for controller weight updates.
Utilized discrete Lyapunov theorem to prove method stability.

Main Results:

The proposed VSS-LMS based adaptive neuro-fuzzy controller significantly improved nonlinear noise suppression.
The method demonstrated superior performance compared to mainstream ANC techniques in simulations.
Stability of the proposed adaptive control system was mathematically verified.

Conclusions:

The novel adaptive neuro-fuzzy controller offers an effective solution for nonlinear ANC.
The VSS-LMS algorithm enhances adaptive learning for improved noise reduction.
This approach provides a robust method for complex noise environments in multi-sensor systems.