Reconstruction Analysis Based Algorithm to Decompose
a Complex System into Subsystems
Keywords
- Model Structuring
- Variable Selection
- Behavioral Modeling
- Model Reduction
- Inductive Modeling
- Fuzzy Inductive Reasoning
Abstract
Two previous papers [
Mirats et al. (2002a)],
[
Mirats et al. (2002b)] were devoted to the selection
of a set of variables that can best be used to model (reconstruct) a given output
variable, whereby only static relations were analysed. Yet even after reducing the
set of variables in this fashion, the number of remaining variables may still be
formidable for large-scale systems. The present paper aims at tackling this
problem by discovering sub-structures within the whole set of the system variables.
Hence whereas previous research dealt with the problem of model reduction by means
of reducing the set of variables to be considered for modeling, the present paper
focuses on model structuring as a means to subdividing the overall modeling task
into subtasks that are hopefully easier to handle. The second and third sections
analyse this problem from a system-theoretic perspective, presenting the
Reconstruction Analysis (RA) methodology, an informational approach to the
problem of decomposing a large-scale system into subsystems. The fourth section uses
the Fuzzy Inductive Reasoning (FIR) methodology to find a possible structure of
a system. The study performed in this paper only considers static relations.
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Last modified: June 15, 2005 -- © François Cellier