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An Inconsistency between the Rate and the Accuracy of the Learning Method for System Identification and its Tracking Characteristics

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Pattern Recognition and Machine Learning

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Abstract

A learning method for system identification has been proposed [1] which is based on the error-correcting training procedure in learning machines [2] and is an iteration method of identifying the dynamic characteristics of a linear system by use of a sampled weighting function, and detailed investigations have already been made on the fundamental characteristics of the method when an unknown system is a stationary linear one, the output of which is not corrupted by noise. A generalized method has also been proposed [3, 4] which improves the rate of convergence using matrix weight.

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References

  1. J. Nagumo and A. Noda, “A Learning Method for System Identification,” IEEE Trans. on Automatic Control, Vol. AC-12, 1967, pp. 282–287.

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  2. B. Widrow, “Adaptive Sampled-Data Systems,” 1959 IRE WESCON Conv. Rec, Pt. 4, pp. 74–85.

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  3. J. M. Mendel, “Gradient, Error-Correction Identification Algorithms,” Information Sciences, 1, 1968, pp. 23–42.

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  4. J. M. Mendel and K. S. Fu, Adaptive, Learning and Pattern Recognition Systems: Theory and Applications, Academic Press, 1970.

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  5. A. Noda, “Effects of Noise and Parameter-Variation on the Learning Identification Method,” Journal of SICE of Japan, 8-5, 1969, pp. 303–312, (in Japanese).

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  6. K. S. Fu, Sequential Methods in Pattern Recognition and Machine Learning, Academic Press, 1968.

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  7. A. E. Albert and L. A. Gardner, Jr., Stochastic Approximation and Nonlinear Regression, MIT Press, 1967.

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  8. A. Noda, “A System Identification by an Adaptive Approximation Method,” 7th Preprint of SICE of Japan, 143, 1968, (in Japanese).

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Author information

Authors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Atsuhiko Noda

Authors
  1. Atsuhiko Noda
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Editor information

Editors and Affiliations

  1. School of Electrical Engineering, Purdue University, Lafayette, Indiana, USA

    K. S. Fu

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© 1971 Plenum Press, New York

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Noda, A. (1971). An Inconsistency between the Rate and the Accuracy of the Learning Method for System Identification and its Tracking Characteristics. In: Fu, K.S. (eds) Pattern Recognition and Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-7566-5_9

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  • DOI: https://doi.org/10.1007/978-1-4615-7566-5_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4615-7568-9

  • Online ISBN: 978-1-4615-7566-5

  • eBook Packages: Springer Book Archive

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