Skip to main content
SpringerLink
Log in

The Suitability of Particle Swarm Optimisation for Training Neural Hardware

  • Conference paper
  • First Online:
Book cover Developments in Applied Artificial Intelligence (IEA/AIE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2358))

Abstract

Learning algorithms implemented for neural networks have generally being conceived for networks implemented in software. However algorithms which have been developed for software implementations typically require far greater accuracy for efficiently training the networks than can be easily implemented in hardware neural networks. Although some learning algorithms designed for software implementation can be successfully implemented in hardware it has become apparent that in hardware these algorithms are generally ill suited, failing to converge well (or at all). Particle Swarm Optimisation (PSO) is known to have a number of features that make it well suited to the training of neural hardware. In this paper the suitability of PSO to train limited precision neural hardware is investigated. Results show that the performance achieved with this algorithm does not degrade until the accuracy of the networks is reduced to a very small number of bits.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Battiti and G. Tecchiolli, „Training Neural Nets with the Reactive Tabu Search“, IEEE Transactions on Neural Networks, 6(5): 1185–1200, 1995.

    Article  Google Scholar 

  2. K. Bennett and J. Blue, „A Support Vector Machine Approach to Decision Trees“, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997

    Google Scholar 

  3. C. Blake, E. Keogh, and C. Merz, „UCI Repository of Machine Learning Databases“, [http://www.ics.uci.edu/~mlearn/MLRepository.html], Irvine, CA, University of California, Department of Information and Computer Science, 1998

    Google Scholar 

  4. M. Clerc, „The swarm and the queen: towards a deterministic and adaptive particle swarm optimization“. Proc. 1999 Congress on Evolutionary Computation, pp 1951–1957, Washington, DC, USA, 1999.

    Google Scholar 

  5. T. Duong, „Learning in Neural Networks: VLSI Implementation Strategies“, Fuzzy Logic and Neural Network Handbook, 1995.

    Google Scholar 

  6. R. Eberhart and J. Kennedy, „A New Optimizer Using Particles Swarm Theory“, Proc. Sixth International Symposium on Micro Machine and Human Science Nagoya, Japan, pp. 39–43, 1995.

    Google Scholar 

  7. R. Eberhart and Y. Shi. „Comparing inertia weights and constriction factors in particle swarm optimisation“. Proceedings of the 2000 congress on evolutionary computation, pp. 84–88, 2000.

    Google Scholar 

  8. M. Glesner and W. Pöchmüller, „An Overview of Neural Networks in VLSI“, Chapman & Hall, London, 1994.

    MATH  Google Scholar 

  9. J. Kennedy, „The Particle Swarm: Social Adaptation of Knowledge“, Proc. IEEE International Conference on Evolutionary Computation Indianapolis, Indiana, USA, pp. 303–308, 1997.

    Google Scholar 

  10. J. Kennedy and R. Eberhart with Y. Shi. „Swarm Intelligence“, San Francisco: Morgan Kaufmann/ Academic Press, 2001.

    Google Scholar 

  11. R. King, C. Feng, and A. Sutherland, „STATLOG: comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence, 9(3):289–334, 1995.

    Article  Google Scholar 

  12. L. Prechelt, „Proben1-A Set of Neural Network Benchmarking Problems and Benchmarking Rules“. Technical Report Nr. 21/ 94. Faculty of Computer Science, University of Karlsruhe, Germany, 1994.

    Google Scholar 

  13. N. Shang and L. Breiman, „Distribution Based Trees Are More Accurate International Conference on Neural Information Processing“. ICONIP’96, p. 133, 1996.

    Google Scholar 

  14. Y. Shang, „Global Search Methods for Solving Nonlinear Optimization Problems„, Ph.D. Thesis, Dept. of Computer Science, Univ. of Illinois, Urbana, Illinois, August 1997.

    Google Scholar 

  15. Y. Shi and R.C. Eberhart, „A Modified Particle Swarm Optimizer“, IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA, 1998.

    Google Scholar 

  16. J. Smith, J. Everhart, W. Dickson, W. Knowlerand and R. Johannes, „Using the ADAP learning algorithm to forecast the onset of diabetes mellitus“,. In Proceedings of the Symposium on Computer Applications and Medical Care, pp. 261–265, 1988.

    Google Scholar 

  17. W. Wolberg and O. Mangasarian, „Multisurface method of pattern separation for medical diagnosis applied to breast cytology“, Proceedings of the National Academy of Sciences, U.S.A., vol. 87, pp 9193–9196, 1990.

    Article  MATH  Google Scholar 

  18. Y. Xie and M. Jabri. Training algorithms for limited precision feedforward neural nets. SEDAL technical report 1991-8-3, Department of Electrical Engineering, University of Sydney, NSW 2006, Australia, 1991.

    Google Scholar 

  19. Y. Xie and M. Jabri, „Analysis of effects of quantisation in multilayer neural networks using statistical models“, IEEE Trans. Neural Networks, 3(2):334–338, 1992.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, Australia

    David Braendler & Tim Hendtlass

Authors
  1. David Braendler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Hendtlass
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, John Street, Hawthorn, Victoria, Australia, 3122

    Tim Hendtlass

  2. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Braendler, D., Hendtlass, T. (2002). The Suitability of Particle Swarm Optimisation for Training Neural Hardware. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-48035-8_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43781-9

  • Online ISBN: 978-3-540-48035-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation