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Identification of complex systems

  • Part C Modeling, Identification And Estimation
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Learning, control and hybrid systems

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 241))

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Abstract

In this paper, we discuss the problem of identifying a complex system with a limited-complexity model using finite corrupted data. Complex systems are ones that cannot be uniformly approximated by a finite dimensional space. Nevertheless, our prejudice is represented by selecting a finitely parameterized set of models from which an estimate of the original system will ultimately be drawn. We will give an account of a new formulation that shows how such a model should be selected from data. We will demonstrate this paradigm on the class of linear time-invariant stable systems and give an overview of the available results concerning input design, consistency, error bounds, and sample complexity.

This research was supported by NSF Grant number 9157306-ECS, AFOSR F49620-950219, and, Siemens AG.

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Authors and Affiliations

  1. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, USA

    Munther A. Dahleh

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  1. Munther A. Dahleh
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Yutaka Yamamoto PhD Shinji Hara PhD

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© 1999 Springer-Verlag London Limited

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Dahleh, M.A. (1999). Identification of complex systems. In: Yamamoto, Y., Hara, S. (eds) Learning, control and hybrid systems. Lecture Notes in Control and Information Sciences, vol 241. Springer, London. https://doi.org/10.1007/BFb0109731

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  • DOI: https://doi.org/10.1007/BFb0109731

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-076-7

  • Online ISBN: 978-1-84628-533-2

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