Skip to main content

Introduction

  • Chapter
  • First Online:
  • 1056 Accesses

Part of the book series: Systems & Control: Foundations & Applications ((SCFA))

Abstract

This book studies the identification of systems in which only quantized output observations are available. The corresponding problem is termed quantized identification.

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
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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. E.R. Caianiello and A. de Luca, Decision equation for binary systems: Application to neural behavior, Kybernetik, 3 (1966), 33–40.

    Article  Google Scholar 

  2. K. Gopalsamy and I.K.C. Leung, Convergence under dynamical thresholds with delays, IEEE Trans. Neural Networks, 8 (1997), 341–348.

    Article  Google Scholar 

  3. A.L. Hodgkin and A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiology, 117 (1952), 500–544.

    Google Scholar 

  4. K. Pakdaman and C.P. Malta, A note on convergence under dynamical thresholds with delays, IEEE Trans. Neural Networks, 9 (1998), 231–233.

    Article  Google Scholar 

  5. H. Abut, Ed., Vector Quantization, IEEE Press Selected Reprint Series, IEEE, Piscataway, 1990.

    Google Scholar 

  6. A.D. Brailsford, M. Yussouff, and E.M. Logothetis, Theory of gas sensors, Sensors and Actuators B, 13 (1993), 135–138.

    Article  Google Scholar 

  7. E.R. Caianiello and A. de Luca, Decision equation for binary systems: Application to neural behavior, Kybernetik, 3 (1966), 33–40.

    Article  Google Scholar 

  8. A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer, Norwell, 1992.

    MATH  Google Scholar 

  9. X. Liu and A. Goldsmith, Wireless communication tradeoffs in distributed control, in Proc. 42nd IEEE Conf. Decision Control, 688–694, 2003.

    Google Scholar 

  10. K. Sayood, Introduction to Data Compression, 2nd ed., Morgan Kaufmann, San Francisco, 2000.

    Google Scholar 

  11. A.M. Sayeed, A signal modeling framework for integrated design of sensor networks, in IEEE Workshop Statistical Signal Processing, 7, 2003.

    Google Scholar 

  12. L. Schweibert and L.Y. Wang, Robust control and rate coordination for efficiency and fairness in ABR traffic with explicit rate marking, Computer Comm., 24 (2001), 1329–1340.

    Article  Google Scholar 

  13. W. Kim, K. Mechitov, J.Y. Choi, and S.K. Ham, On tracking objects with binary proximity sensors, in Information Processing in Sensor Networks, Fourth International Symposium, 301–308, 2005.

    Google Scholar 

  14. J. Sun, Y. Kim, and L.Y. Wang, HEGO signal processing and strategy adaptation for improved performance in lean burn engines with a lean NOx trap, Int. J. Adaptive Control Signal Process., 18 (2004), 145–166.

    Article  Google Scholar 

  15. H.F. Chen and L. Guo, Identification and Stochastic Adaptive Control, Birkhäuser, Boston, 1991.

    MATH  Google Scholar 

  16. H.J. Kushner and G. Yin, Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., Springer-Verlag, New York, 2003.

    MATH  Google Scholar 

  17. M. Milanese and A. Vicino, Optimal estimation theory for dynamic systems with set membership uncertainty: An overview, Automatica, 27 (1991), 997–1009.

    Article  MATH  MathSciNet  Google Scholar 

  18. V.N. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, 1998.

    MATH  Google Scholar 

  19. V.N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed. Springer-Verlag, New York, 2000.

    MATH  Google Scholar 

  20. M. Vidyasagar, Learning and Generalization: With Applications to Neural Networks, 2nd ed., Springer, London, 2003.

    MATH  Google Scholar 

  21. T. Wang, R.E. Soltis, E.M. Logothetis, J.A. Cook, and D.R. Hamburg, Static characteristics of ZrO2 exhaust gas oxygen sensors, SAE paper 930352, 1993.

    Google Scholar 

  22. L. Xiao, M. Johansson, H. Hindi, S. Boyd, and A. Goldsmith, Joint optimization of communication rates and linear systems, IEEE Trans. Automat. Control, 48 (2003), 148–153.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Le Yi Wang .

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Wang, L.Y., Yin, G.G., Zhang, JF., Zhao, Y. (2010). Introduction. In: System Identification with Quantized Observations. Systems & Control: Foundations & Applications. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4956-2_1

Download citation

Publish with us

Policies and ethics