Genetic programming for model selection of TSK-fuzzy
systems
Information Sciences, 136 (1-4):
7--28 (August 2001)Abstract
This paper compares a genetic programming (GP)
approach with a greedy partition algorithm (LOLIMOT)
for structure identification of local linear
neuro-fuzzy models. The crisp linear conclusion part of
a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the
underlying model in the local region specified in the
premise. The objective of structure identification is
to identify an optimal partition of the input space
into Gaussian, axis-orthogonal fuzzy sets. The linear
parameters in the rule consequent are then estimated by
means of a local weighted least-squares algorithm.
LOLIMOT is an incremental tree-construction algorithm
that partitions the input space by axis-orthogonal
splits. In each iteration it greedily adds the new
model that minimizes the classification error. GP
performs a global search for the optimal partition tree
and is therefore able to backtrack in case of
sub-optimal intermediate split decisions. We compare
the performance of both methods for function
approximation of a highly non-linear two-dimensional
test function and an engine characteristic map.
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