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System Identification using Combined FIR and Functional Link Neural Networks: Application to ANC Secondary Path
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 modify linear structure and develop proper algorithm for both the control and model structures employed in ANC. Recently, adaptive combination of finite impulse response filter and functional link artificial neural networks (CFFLANN) structure was proposed by Zhao and Zhang (2009) to compensate linear and nonlinear distortions in nonlinear communication channel. This paper presents a real-time implementation of CFFLANN structure to model the secondary path of nonlinear active noise control system by using DSP TMS320C6713. To examine the performance of this structure, we apply the various types of functional expansion to model linear and nonlinear distortions in the nonlinear secondary path by comparing it with LMS based FIR filter. Identification results show that the CFFLANN can model the nonlinear secondary path with satisfied modeling error.
Identification, CFFLANN, Secondary path nonlinearity, ANC.
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