System Theory pp 391-403 | Cite as

Categories of Nonlinear Dynamic Models

  • Ronald K. Pearson
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 518)


Many industrially-promising model-based control strategies require nonlinear, discrete-time dynamic models of restricted complexity relative to that of detailed mechanistic descriptions. One possible route to these models is empirical, permitting explicit control of model complexity through the class C of approximating models considered. Conversely, if this choice is not made with sufficient care, the resulting model may fit available input/output data reasonably well but violate certain important behavioral requirements (e.g., stability, monotonicity of step responses, etc.). This paper proposes category theory as a general framework for simultaneously considering both the structural and behavioral aspects of empirical model identification. Two particular advantages of this use of category theory are first, that it forces the consideration of models and input sequences together and second, that it provides a simple framework for examining the behavioral differences between model classes.


Input Sequence Nonlinear Dynamic Model Linear Dynamic Model Hammerstein Model Identity Morphism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Ronald K. Pearson
    • 1
  1. 1.ETH ZürichInstutut für AutomatikZürichSwitzerland

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