Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/17423/ http://ir.unimas.my/17423/ http://ir.unimas.my/17423/ http://ir.unimas.my/17423/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20Produce%20Monotone%20Fuzzy%20%28abstract%29.pdf |
| Summary: | In this paper, ad hoc and system identification
methods are used to generate fuzzy If-Then rules for a zeroorder
Takagi-Sugeno-Kang (TSK) Fuzzy Inference System
(FIS) using a set of multi-attribute monotone data. Convex
and normal trapezoidal fuzzy sets, with a strong fuzzy
partition strategy, is employed. Our analysis shows that even
with multi-attribute monotone data, non-monotone fuzzy If-
Then rules can be produced using an ad hoc method. The
same observation can be made, empirically, using a system
identification method, e.g., a derivative–based optimization
method and the genetic algorithm. This finding is important
for modeling a monotone FIS model, as the result shows that
even with a “clean” data set pertaining to a monotone system,
the generated fuzzy If-Then rules may need to be preprocessed,
before being used for FIS modeling. As such,
monotone fuzzy rule relabeling is useful. Besides that, a
constrained non-linear programming method for FIS
modelling is suggested, as a variant of the system identification
method. |
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