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Adaptive Noise Cancelation Using Fuzzy Brain Emotional Learning Network

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Advances in Computational Intelligence Systems (UKCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 650))

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Abstract

This paper proposes a fuzzy brain emotional learning network for adaptive noise cancelation. The proposed network is based on brain emotional learning algorithm which is developed according to the emotional learning process of mammalian and the fuzzy inference is added for better ability to handle uncertainties. Parameters in the network are modified online by the derived adaption laws. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, in order to demonstrate the performance of the proposed filter, it is applied in a signal processing application where different source signals and noise signals are used. A comparison between the proposed method, Least mean square algorithm and a fuzzy cerebellar model articulation controller filter shows that the proposed method can converge faster even when the source signal is corrupted severely.

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of the Brain-like Computing, Cognitive Science Department, School of Informatics, Xiamen University, Xiamen, Fujian, People’s Republic of China

    Qianqian Zhou, Chih-Min Lin & Fei Chao

  2. Department of Electrical Engineering, Yuan Ze University, Chung-Li, 320, Tao-Yuan, Taiwan

    Chih-Min Lin

Authors
  1. Qianqian Zhou
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  2. Chih-Min Lin
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  3. Fei Chao
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Corresponding author

Correspondence to Fei Chao .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

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Zhou, Q., Lin, CM., Chao, F. (2018). Adaptive Noise Cancelation Using Fuzzy Brain Emotional Learning Network. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-66939-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66938-0

  • Online ISBN: 978-3-319-66939-7

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