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Adaptive Wavelet Extreme Learning Machine (AW-ELM) for Index Finger Recognition Using Two-Channel Electromyography

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

This paper proposes a new structure of wavelet extreme learning machine i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM0 and sigmoid (Sig-ELM) activation function. The experimental results demonstrate that the proposed AW-ELM performs better than W-ELM and Sig-ELM.

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References

  1. Zhou, B., Shi, A., Cai, F., Zhang, Y.-S.: Wavelet neural networks for nonlinear time series analysis. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 430–435. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Lin, C.-J., Tsai, H.-M.: FPGA implementation of a wavelet neural network with particle swarm optimization learning. Mathematical and Computer Modelling 47(9-10), 982–996 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  3. Huang, G.B., et al.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B, Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  4. Cao, J., Lin, Z., Huang, G.-B.: Composite function wavelet neural networks with extreme learning machine. Neurocomputing 73(7), 1405–1416 (2010)

    Article  Google Scholar 

  5. Salih, D.M., et al.: Wavelet network based online sequential extreme learning machine for dynamic system modeling. In: 2013 9th Asian Control Conference (ASCC) (2013)

    Google Scholar 

  6. Ling, S.-H., et al.: Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging. IEEE Transactions on Industrial Electronics 55(9), 3447–3460 (2008)

    Article  Google Scholar 

  7. Javed, K., Gouriveau, R., Zerhouni, N.: SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing 123, 299–307 (2014)

    Article  Google Scholar 

  8. Anam, K., Al-Jumaily, A.A.: Swarm-based extreme learning machine for finger movement recognition. In: 2014 Middle East Conference on Biomedical Engineering (MECBME) (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Jember, Indonesia

    Khairul Anam

  2. University of Technology Sydney, Australia

    Khairul Anam & Adel Al-Jumaily

Authors
  1. Khairul Anam
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  2. Adel Al-Jumaily
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Anam, K., Al-Jumaily, A. (2014). Adaptive Wavelet Extreme Learning Machine (AW-ELM) for Index Finger Recognition Using Two-Channel Electromyography. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_59

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_59

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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