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System Identification of Secondary Path in ANC using Combined FIR and Functional Link Artificial Neural Network

R. Y. Redi, Riyanto T. Bambang


The finite impulse response (FIR) filter with least mean square (LMS) algorithm has widely used in linear active noise control (ANC) system because it is relatively simple to design and implement. For nonlinear case, such as when the control signals excite the secondary speaker saturation, their performances are known to be deteriorating, we must modificate linear structure and develope proper algorithm for both the control and model structures employed in ANC. Recently, combination of finite impulse response filter and functional link artificial neural networks
(CFFLANN) is presented to compensate linear and nonlinear distortions in nonlinear communication channel[1]. In this paper we apply this simple structure to model linear and nonlinear distortions in the nonlinear secondary path. To examine the performance of this structure, we apply the various types of functional expansion[1,2,4] by comparing it with LMS based FIR filter. Identification results by using experimental measurement ANC data show that the CFFLANN can model the secondary path with satisfied modeling error.

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