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Digital Implementation of OS-ELM for Data Classification in Real-Time

  • Susanta Kumar RoutEmail author
  • Pradyut Kumar Biswal
Conference paper
  • 9 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)

Abstract

Field-programmable gate array (FPGA) has been used as a very effective hardware platform in different research area as it enhances the efficiency of the embedded module. The accessibility of minimized, fast circuitry for a artificial neural networks (ANNs) is the most important and utmost necessity for many critical applications. In this paper, a single layer feed-forward neural network (SLFN) named as online sequential extreme learning machine (OS-ELM) is conferred and realized in digital platform for real-world data classification. The digital employment of OS-ELM supports to form an efficient hardware unit for data classification in real-time as the classifier has high learning speed and promising accuracy. Finally, the digital architecture of OS-ELM is implemented on a Virtex-5 FPGA hardware platform to validate the feasibility, efficacy, and vitality of the proposed classifier in real-time.

Keywords

Field-programmable gate array (FPGA) Artificial neural networks (ANNs) Single layer feed-forward neural network (SLFN) Online sequential extreme learning machine (OS-ELM) Digital architecture Hardware implementation 

References

  1. 1.
    Misra, J., Saha, I.: Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1–3), 239–255 (2010)CrossRefGoogle Scholar
  2. 2.
    Dlugosz, R., Talaska, T., Pedrycz, W.: Current-mode analog adaptive mechanism for ultra-low-power neural networks. IEEE Trans. Circuits Syst. II Express Briefs 58(1), 31–35 (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Shibata, T.: A neuron-MOS-based VLSI implementation of pulse-coupled neural networks for image feature generation. IEEE Trans. Circuits Syst. I Regul. Pap. 57(6), 1143–1153 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Arena, P., et al.: A CNN-based chip for robot locomotion control. IEEE Trans. Circuits Syst. I Regul. Pap. 52(9), 1862–1871 (2005)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Li, Z., Feng, L., Bai, H., Wang, C.: Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection. IET Circ. Dev. Syst. 12(1), 108–115 (2018)CrossRefGoogle Scholar
  6. 6.
    Huang, G.B., Babri, H.A.: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans. Neural Networks 9(1), 224–229 (1998)CrossRefGoogle Scholar
  7. 7.
    Huang, G.B.: Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Networks 14(2), 274–281 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990 (2004)Google Scholar
  9. 9.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRefGoogle Scholar
  10. 10.
    Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)CrossRefGoogle Scholar
  11. 11.
    Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2(2), 107–122 (2011)CrossRefGoogle Scholar
  12. 12.
    Liang, N.Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Networks 17(6), 1411–1423 (2006)CrossRefGoogle Scholar
  13. 13.
    Jianwei, Z., Wang, Z., Park, D.S.: Online sequential extreme learning machine with forgetting mechanism. Neurocomputing 87, 79–89 (2012)CrossRefGoogle Scholar
  14. 14.
    Ye, Y., Squartini, S., Piazza, F.: Online sequential extreme learning machine in nonstationary environments. Neurocomputing 116, 94–101 (2013)CrossRefGoogle Scholar
  15. 15.
    Basu, A., et al.: Silicon spiking neurons for hardware implementation of extreme learning machines. Neurocomputing 102, 125–134 (2013)CrossRefGoogle Scholar
  16. 16.
    Decherchi, S., Gastaldo, P., Leoncini, A., Zunino, R.: Efficient Digital Implementation of Extreme Learning Machines for Classification. IEEE Trans. Circuits Syst. II Express Briefs 59(8), 496–500 (2012)CrossRefGoogle Scholar
  17. 17.
    Frances-Villora, J.V., Rosado-Muñoz, A., Martínez-Villena, J.M., Bataller-Mompean, M., Guerrero, J.F., Wegrzyn, M.: Hardware implementation of real-time Extreme Learning Machine in FPGA: analysis of precision, resource occupation and performance. Comput. Electr. Eng. 1(51), 139–156 (2016)CrossRefGoogle Scholar
  18. 18.
    Sahani, M., Dash, P.K.: Variational mode decomposition and weighted online sequential extreme learning machine for power quality event patterns recognition. Neurocomputing 310, 10–27 (2018)CrossRefGoogle Scholar
  19. 19.
    Blake, C., Merz, C.J.: fUCIg Repository of Machine Learning Databases (1998)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.International Institute of Information TechnologyBhubaneswarIndia
  2. 2.Siksha “O” Anusandhan deemed to be UniversityBhubaneswarIndia

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