Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Research on Performance Improvement of the Adaptive Active Noise Control System Using the Recurrent Neural Network

순환형 신경망을 이용한 적응형 능동소음제어시스템의 성능 향상에 대한 연구

  • Received : 2010.06.17
  • Accepted : 2010.08.13
  • Published : 2010.08.31
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Abstract

The performance of noise attenuation of the adaptive active noise control algorithm is improved using the recurrent neural network. The FXLMS that has been frequently used in the active noise control is simple and has low computational load, but this method is weak to nonlinearity of the main or secondary path since it is based on the FIR linear filter method. In this paper, the recurrent neural network filter has been developed and applied to improvement of the active noise attenuation by simulation.

순환형 신경망을 이용한 적응형 능동소음 제어시스템의 소음저감 성능을 개선한다. 능동소음제어에 가장 많이 사용되고 있는 FXLMS는 단순하고 계산 부담이 적지만 FIR 선형필터에 기반을 둔 필터링 방법이어서 주 경로 또는 2차 경로의 비선형특성에 취약한 단점이 있다. 본 연구에서는 이러한 비선형 특성과 불확실성에 대해 강인한 특성을 갖는 순환형 신경망 필터링 기법을 개발하여 능동소음 제어시스템의 소음 저감 성능을 개선함을 시뮬레이션을 통해 보인다.

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References

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