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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 128–134Cite as

Recursive Complex Extreme Learning Machine with Widely Linear Processing for Nonlinear Channel Equalizer

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

Recently, a new learning algorithm for the feedforward neural network named the complex extreme learning machine (C-ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we propose a new widely linear recursive C-ELM algorithm for nonlinear channel equalizer. The proposed algorithm improves its performance especially in case of real valued modulation such as BPSK and PAM. The computer simulation results demonstrate the improvement in performance achievable with the proposed equalization algorithm.

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Author information

Authors and Affiliations

  1. Dept. of Electronics Eng., Sejong University, Kunja, Kwangjin, 98, 143-747, Seoul, Korea

    Junseok Lim

  2. Samsung Electronics Co.,Ltd,  

    Jaejin Jeon

  3. LG Electronics Inc,  

    Sangwook Lee

Authors
  1. Junseok Lim
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  2. Jaejin Jeon
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  3. Sangwook Lee
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Lim, J., Jeon, J., Lee, S. (2006). Recursive Complex Extreme Learning Machine with Widely Linear Processing for Nonlinear Channel Equalizer. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_19

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  • DOI: https://doi.org/10.1007/11760191_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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