| Summary: | 碩士 === 大同工學院 === 資訊工程學系 === 85 === The purpose of this thesis is to propose a systematic and
effective method toconstruct different types of fuzzy models
such as Tsukamoto-type, Mamdani-type,and Takagi-Sugeno-Kang-type
fuzzy models. The main processes of the proposedsystematic fuzzy
modeling scheme are as follows. The first step is to
fuzzycluster the given data and then to find the cluster
centers. However, thetraditional clustering methods such as
fuzzy c-means algorithm and mountainclustering method, when the
given data are too concentrated on a compact area,the cluster
centers cannot be well estimated due to the mutual effect
ofneighboring data. Therefore, a modified clustering algorithm
is developed toovercome this drawback. In the developed
algorithm, the center of the mostsatisfied subset is assigned to
the considered cluster center after tuningthe centers several
times. Later, the subset with the center just identified
istemporarily removed to eliminate the effects of this cluster
to the others. Bymeans of this scheme, we can achieve not only
the goal of fast clusterung butalso good clustering performance.
The second step is to establish a fuzzy rulebase. The centers of
the membership functions are obtained by the projectionof the
cluster centers onto the axes of their own coordinate. By
comparingthe maching degrees, we can gradually build the
necessary fuzzy rules for therule base. The final step is to
tune the widths and the centers of the triangular membership
functions via the genetic algorithms and the gradientdescent
method. Since the center of area defuzzification method is not
allowedto take the partial derivatives, the parameters of such
membership functionscannot be tuned by the gradient descent
method. Therefore, the defuzzificationof the Mamdani-type fuzzy
model is reformulated to solve the partial derivativeproblem.
Based on the proposed method, we perform simulations for various
commonly used examples. The simulation results are given. Based
on the simulation results, we can find that the proposed fuzzy
modeling approachesprovides very satisfactory performance.
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