Skip to main content

Using a Real-Time Web-Based Pattern Recognition System to Search for Component Patterns Database

  • Conference paper
  • First Online:
  • 285 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2510))

Abstract

Faraway engineers are able to sketch direct the shape of engineering components by the browser, and the recognition system will proceed with search for the component database of company by the Internet. In this paper, component patterns are stored in the database system. Component patterns with the approach of database system will be able to improve the capacity of recognition system effectively. In our approach, the recognition system adopts distributed compute, and it will raise the recognition rate of system. The system uses a recurrent neural network (RNN) with associative memory to perform the action of training and recognition. The final phase joins the technology of database match in process of the recognition except distributed compute, and it will solve the problem of spurious state. In this paper, our system will be carried out in the Yang-Fen Automation Electrical Engineering Company. The plan of experiment has gone through four months, and their engineers are also used to take advantage of the way of Web-Based pattern recognition.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Singh, S., “A Long Memory Pattern Modeling and Recognition System for Financial Forecasting,” Pattern Analysis and Applications, vo1. 2, no. 3, (1999) 264–273.

    Article  Google Scholar 

  2. S. Kak, “Better Web Searches and Prediction with Instantaneously Trained Neural Networks” IEEE Intelligent Systems, vol. 14, no. 6, (1999) 78–81.

    Google Scholar 

  3. R.P.W. Duin, “Superlearning and neural network magic,” Pattern Recognition Letters, vol. 15, 1994, pp. 215–217.

    Article  Google Scholar 

  4. M.A. Kraaijveld and R.P.W. Duin, “The effective capacity of multilayer feedforward network classifiers,”Proc.12th Int’l Conf. on Pattern Recognition.(ICPR 94), Israel, vol. B,(1994). 99–103.

    Google Scholar 

  5. Z. TAN and M.K. ALI, “Pattern recognition with stochastic resonance in a generic neural network,” International Journal of Modern Physics C, vo1. 11, no. 8, (2000)1585–1593.

    Article  MATH  Google Scholar 

  6. M. Perus, “Neural networks as a basis for quantum associative networks,” Neural Network World, vol. 10, no. 6, (2000) 1001–1013.

    Google Scholar 

  7. Brouwer, R.K., “An Integer Recurrent Artificial Neural Network for classifying Feature Vectors,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 14, no. 3, (2000) 339–335.

    Article  Google Scholar 

  8. Brouwer, R.K., “A Fuzzy Recurrent Artificial Neural Network for Pattern classification, “International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vo1. 8, no. 5, (2000) 525–538.

    Article  MATH  Google Scholar 

  9. Kamp, Y. and Hasler, M., Recursive Neural Networks for Associative Memory,: Wiley-Interscience Series in Systems and Optimization, England, (1990) 10–34.

    Google Scholar 

  10. V. Gimenez, L. Aslanyan, J. Catellanos, and V. Ryazanov. “Distribution Functions as Attractor for Recurrent Neural Networks,” Pattern Recognition and Image Analysis. vol. 11, no. 3, (2001) 492–497.

    Google Scholar 

  11. J.J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. the National Academy of sciences, USA,vol. 79, (1982) 2554–2558..

    Article  MathSciNet  Google Scholar 

  12. Simon Haykin, Neural networks a comprehensive foundation, 2nd, Macmillan College Publishing Company, Inc., New York (1999).

    MATH  Google Scholar 

  13. J.J. Hopfield and D.W. Tank, “Computing with neural circuits: a model,” Science, vol. 233, (1986) 625–633.

    Article  Google Scholar 

  14. B. Mueller, J. Reinhardt, and M. T. Strickland, Neural Networks, Springer-Verlag, Berlin Heidelberg (1995).

    MATH  Google Scholar 

  15. Zurada, J.M., Artificial Neural Systems, West Publishing, St. Paul, UN. (1992).

    Google Scholar 

  16. Lippmann, R.P., “An Introduction to Computing with Neural Nets,” IEEE ASSP Mag., (1987)4–22, also reprinted in neural networks: Theoretical Foundations and Analysis, edited by C. Lau, IEEE Press, New York, (1992) 5-23.

    Google Scholar 

  17. W. A. Little and G.. L. Shaw, “Analytical study of the memory storage capacity of a neural network, “Mathematical Biosciences, vo1. 39,no. 1, (1978) 281–290.

    Article  MATH  MathSciNet  Google Scholar 

  18. Simon Haykin, Neural networks a comprehensive foundation, Macmillan College Publishing Company, Inc., New York(1994).

    MATH  Google Scholar 

  19. Jinwen. Ma, “A Neural Network Approach to real-time pattern recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 6, 2001, pp. 934–947.

    Google Scholar 

  20. Ma, J.W., “The stability of the generalized Hopfield networks in randomly asynchronous mode,” Neural Networks, vol. 10, no. 6, (1997) 1109–1116.

    Article  Google Scholar 

  21. R.E. McEliece, E.C. Posner, E.R. Rodernich and S.S. VenKatesh, “The capacity of the Hopfield associative memory, “IEEE Trans. Inform. In.IT, vol. 33, no. 2, (1987) 461–483.

    Article  MATH  Google Scholar 

  22. L.F. Abbott and T.B. Kepler, “Optimal learning in neural network memories, “J.Phys. A:Math. General, vol. 22, (1989) 711–717.

    Article  MathSciNet  Google Scholar 

  23. S.S. Venkatesh and D. Pitts, “Linear and logarithmic capacities in associative memory, “IEEE Trans. Inform. Th. IT, vol. 35, (1989) 558–568.

    Article  Google Scholar 

  24. D.J. Amit, Modeling Brain Function: The World of Attractor Neural Networks, Cambridge University Press, Net York (1989).

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hsiao, SJ., Fan, KC., Sung, WT., Ou, SC. (2002). Using a Real-Time Web-Based Pattern Recognition System to Search for Component Patterns Database. In: Shafazand, H., Tjoa, A.M. (eds) EurAsia-ICT 2002: Information and Communication Technology. EurAsia-ICT 2002. Lecture Notes in Computer Science, vol 2510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36087-5_86

Download citation

  • DOI: https://doi.org/10.1007/3-540-36087-5_86

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00028-0

  • Online ISBN: 978-3-540-36087-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics