Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Discrete-Time Dynamic Models: Structure and Behavior


R. Pearson

Tallinn, Estonia, Institute of Cybernetics

Discrete-time dynamic models arise in many application areas, including process modeling for computer control, time-series analysis (e.g., for economic forcasting), and digital signal processing, to name only a few. These models may be defined in either of two distinct ways: behaviorally, in terms of observed input/output behavior, or structurally, in terms of explicit predictive representations. In the case of linear models, the behavioral description is the principle of superposition and the structural description leads to the impulse response or convolution representation. Further, the equivalence of these two descriptions follows from Cauchy's functional equation. In contrast, these approaches are not equivalent in the case of nonlinear discrete-time dynamic models and this talk considers examples of well-defined nonlinear model classes based on both behavioral and structural descriptions. The focus of this talk is on structure-behavior relations and connections are made with physical system modeling, digital signal processing, and functional equations.


Type of Publication:

(05)Plenary/Invited/Honorary Lecture

File Download:

Request a copy of this publication.
(Uses JavaScript)
% No recipe for automatically generating a BibTex entry for (05)Plenary/Invited/Honorary Lecture
Permanent link