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Towards a harmonic blending of deterministic and stochastic frameworks in information processing

  • Part I Identification For Robust Control
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Robustness in identification and control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 245))

Abstract

This paper presents some preliminary efforts towards a new methodology which harmonically blends deterministic and stochastic frameworks in information processing, including identification, signal processing, communications, system design, etc. We begin with a discussion on distinctive features of the two frameworks and explanation of compelling reasons and motivating issues for introducing such a combined framework. Using persistent identification as an example, we demonstrate the application and utility of the methodology. Lower and upper bounds on identification errors are obtained for systems subject to both deterministic unmodelled dynamics and random external disturbances.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Wayne State University, 48202, Detroit, MI

    Yi Le Wang

  2. Department of Mathematics, Wayne State University, 48202, Detroit, MI

    George Yin

Authors
  1. Yi Le Wang
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  2. George Yin
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Editor information

A. Garulli (Assistant Professor)A. Tesi (Assistant Professor)

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© 1999 Springer-Verlag London Limited

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Le Wang, Y., Yin, G. (1999). Towards a harmonic blending of deterministic and stochastic frameworks in information processing. In: Garulli, A., Tesi, A. (eds) Robustness in identification and control. Lecture Notes in Control and Information Sciences, vol 245. Springer, London. https://doi.org/10.1007/BFb0109863

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  • DOI: https://doi.org/10.1007/BFb0109863

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-179-5

  • Online ISBN: 978-1-84628-538-7

  • eBook Packages: Springer Book Archive

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