Open Access Open Access  Restricted Access Subscription Access

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

R. Y. Redi, Riyanto T. Bambang

Abstract


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.

Full Text:

PDF

References


H. Zhao and J. Zhang, Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel, IEEE Transactions On Neural Networks, Vol. 20, No. 4, April 2009.

G. Panda and D. P. Das, Functional Link Artificial Neural Network for Active Control of Nonlinear Processes, International workshop on Acoustic Echo and Noise Control (IWAENC2003), Kyoto, Japan, Sept. 2003.

D. Zhou, and V. D. Brunner, Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control, IEEE Transactions On Circuits And Systems - I: Regular Papers, Vol. 54, No. 3, March 2007.

B. T. Rao, B. Sameet, G. K. Swathi, K. V. Gupta, Ch. R. Teja, S. Sumana, A novel neural network approach for software cost estimation using FLANN, IJCSNS International journal of computer science and network security, vol. 9 No. 6, June 2009.

L. Tan and J. Jiang, Adaptive volterra filters for active control of nonlinear noise processes, IEEE Trans. Signal Process., vol. 49, no. 8, pp. 1667-1676, Aug. 2001.

S. M. Kuo and D. R. Morgan, Active noise control systems - Algorithms and DSP implementations, New York, John Wiley, 1996.

R. Y. Redi, Riyanto T. Bambang, Nonlinear active noise control using Combined FIR and Functonal link artificial neural networks., in preparation.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2010.v6.487

Refbacks

  • There are currently no refbacks.