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DSP Implementation of Combined FIR-Functional Link Neural Network for Active Noise Control

Redi R. Yacoub, Riyanto T. Bambang, Agung Harsoyo, Joko Sarwono


This paper presents theoretical and experimental study of active noise control (ANC) system based on the combination of finite-impulse response filter and functional-link neural network (CFFLNN). The combined structure is used in an effort not only to compensate for the nonlinear phenomena that often appear in the real-world ANC applications, but also to improve the attenuation performance. In this paper, we propose the development of adaptive learning algorithm, both for an identification of ANC secondary-path and for the active noise control process. Implementation of the active noise control process and proposed algorithm on digital signal processors DSP TMS320C6713 DSK is performed for real-time experiments in ANC system. Experimental results of CFFLNN-based ANC applied to the duct noise system show that the attenuation performance can be improved by introducing the FIR subsection, and that FLNN subsection can effectively compensate the nonlinearity in the ANC system.


ANC, nonlinear phenomena, CFFLNN, learning algorithm

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