Intelligent IIR Filter based Multiple-Channel ANC Systems

지능형 IIR 필터 기반 다중 채널 ANC 시스템

  • 조현철 (울산과학대학 전기전자학부 전기) ;
  • 여대연 (동아대학교 전기공학과) ;
  • 이영진 (한국폴리텍 항공대학 항공전기과) ;
  • 이권순 (동아대학교 전기공학과)
  • Received : 2010.07.13
  • Accepted : 2010.11.10
  • Published : 2010.12.01


This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.


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