Identification of complex systems

  • Munther A. Dahleh
Part C Modeling, Identification And Estimation
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 241)


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.


Finite Dimensional Subspace Unmodeled Dynamic Minimum Prediction Error White Noise Model White Noise Signal 
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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Munther A. Dahleh
    • 1
  1. 1.Laboratory for Information and Decision SystemsMassachusetts Institute of TechnologyUSA

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