Structure identification for complex system and inference interpretable rules
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Date
2012
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The large and complex systems are
characterized by the difficulty of formalizing and the
lack of expertise. They are built from data
representing instances of the system. The drawback
of these structure selection methods is that pay
particular attention to the numerical accuracy of the
resulting model and little attention to the qualitative
and semantic aspect. The use of a large number of
input variables results in an introduction of
redundant elements, poor transparency and an
excessive complexity of the model obtained. To solve
these problems, a particular interest should be given
for selecting relevant input variables that can provide
a reasonable compromise between the quality of
approximation, the complexity and the transparency
of the model. The proposed approach for structure
selection assumes constraints on the number of
variables to be selected in the initial combination to
describe the model. Relevant inputs are found from
input-output data and variables with the highest
correlation coefficient with the output variable are
selected in the initial combination
Description
Keywords
Fuzzy inference system, Complex systems, Extraction rule, Learning, Induction rule, Interpretive rule, Partitioning