Reasoning about Input-Output Modeling of Dynamical Systems

  • Matthew Easley
  • Elizabeth Bradley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


The goal of input-output modeling is to apply a test input to a system, analyze the results, and learn something useful from the causee ffect pair. Any automated modeling tool that takes this approach must be able to reason effectively about sensors and actuators and their interactions with the target system. Distilling qualitative information from sensor data is fairly easy, but a variety of difficult control-theoretic issues — controllability, reachability, and utility — arise during the planning and execution of experiments. This paper describes some representations and reasoning tactics, collectively termed qualitative bifurcation analysis, that make it possible to automate this task.


Cell Dynamic Test Input Drive Frequency Qualitative Reasoning Qualitative Simulation 
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-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Matthew Easley
    • 1
  • Elizabeth Bradley
    • 1
  1. 1.Department of Computer ScienceUniversity of ColoradoBoulder

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