Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network

Abstract

This article aims to develop a more efficient adaptive filter for the active noise cancellation (ANC). A novel recurrent interval type-2 fuzzy brain emotional learning filter (RT2BELF) is proposed for achieving favourable filtering performance. The ANC is a method to eliminate noise by creating an anti-noise signal which has the same magnitude but opposite phase with the unwanted noise. In order to adapt to the change of the noise, the parameters for the RIT2BELF are online updated based on the adaptive laws, which are derived by the steepest descent gradient approach. The performance of the proposed ANC design method is successfully demonstrated based on numerical simulation results in the real signals. Finally, the superiority of the proposed method is confirmed by the results comparison with some noise cancellation methods.

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Acknowledgements

The authors appreciate the financial support in part from the Ministry of Science and Technology of Republic of China under Grant MOST 106-2221-E-155-002-MY3.

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Correspondence to Chih-Min Lin.

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Le, T., Huynh, T. & Lin, C. Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network. Neural Comput & Applic 32, 8725–8734 (2020). https://doi.org/10.1007/s00521-019-04366-8

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Keywords

  • Active noise cancellation
  • Type-2 fuzzy system
  • Brain emotional learning network
  • Recurrent neural network