FUZZY CLUSTERING, GENETIC ALGORITHMS AND NEURO ... - ITM

624 P.F. VAN LITH et al.
functions in such a way that interpretation was often difficult, while the
location of the membership functions was changed very little. This
makes the method very sensitive to initialization. Consequently, the
other two identification methods are more desirable alternatives.
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10. CONCLUSIONS
In this paper, we presented a successful approach to constructing
hybrid fuzzy-first principles models from process data. Three different
identification techniques for the construction of the fuzzy submodel
for the net growth rate were applied successfully and compared.
Of these three, fuzzy clustering was found to be the best alternative,
because it determines the fuzzy model structure itself from the process
data at hand. The cluster-like connected rule base results in a transparent
and interpretable fuzzy submodel with excellent simulation
performance.
A genetic algorithm approach is a good alternative to fuzzy clustering,
with cluster-like connected rule bases giving good results.
Although the model is less interpretable than with fuzzy clustering,
the approach has the advantage that it can be applied so that no
input-output data or estimations of the uncertain relation need to be
available, which is an advantage above fuzzy clustering. However, in
this case, a deliberation needs to be made between the excellent transparency
of fuzzy models constructed with clustering (but additional
uncertainty introduced by estimations) and the somewhat lesser interpretability
of fuzzy submodels constructed with a GA (but with no
additional uncertainty caused by estimations).
The major drawback of ANFIS is its sensitivity to initialization of
the problem. As with GA, the structure must be provided, as an initial
guess of membership parameters. An optimum is found from there.
Other neuro-fuzzy approaches, like ASMOD and NEFPROX, provide
mechanisms of determining the model structure automatically. These
mechanisms could be incorporated in the ANFIS technique. Given
the requirements for this research, genetic algorithms and fuzzy
clustering provide good alternatives, with fuzzy clustering the best
option, if input–output data or estimations are available.