Filtered-X Adaptive Neuro-Fuzzy Inference Systems for Nonlinear Active Noise Control

Authors:
Riyanto T. Bambang
Affiliations:
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesha 10, Bandung 40132, Indonesia
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 6
Cited 1

Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Active Noise Control Systems: Algorithms and DSP Implementations

Active Noise Control Systems: Algorithms and DSP Implementations
Improved training of neural networks for the nonlinear active control of sound and vibration

IEEE Transactions on Neural Networks
Generalization of adaptive neuro-fuzzy inference systems

IEEE Transactions on Neural Networks
New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks

IEEE Transactions on Neural Networks

Nonlinear active noise control using EKF-based recurrent fuzzy neural networks

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new method for active noise control is proposed and experimentally demonstrated. The method is based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which is introduced to overcome nonlinearity inherent in active noise control. A new algorithm referred to as Filtered-X ANFIS algorithm suitable for active noise control is proposed. Real-time experiment of Filtered-X ANFIS is performed using floating point Texas Instruments C6701 DSP. In contrast to previous work on ANC using computational intelligence approaches which concentrate on single channel and off-line adaptation, this research addresses multichannel and employs online adaptation, which is feasible due to the computing power of the DSP.