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Edi Shmukler. Anytime Fuzzy Control. Master's Thesis, Bar Ilan University,2006.
Fuzzy logic has been successfully applied in various fields. However, as fuzzy controllers increase in size and complexity, the number of control rules increases exponentially and real-time behavior becomes more difficult. This thesis introduces an any-time fuzzy controller. Much work has been done to optimize and speed up a controlling process, however none of the existing solutions provides an any-time behavior. This study first introduces several constraints that should be satisfied in order to guarantee an any-time behavior. These constraints are related to aggregation and defuzzification phases of fuzzy control. Popular aggregation (max-min, sum-product) and defuzzification methods (mean-of-maxima (MOM) and center-of-gravity (COG)) are first shown to satisfy these constraints, and then three linearization methods are presented. Linearization methods are used to reorder fuzzy rule-bases such that a reordered rule-base would result in any-time behavior. Finally, several approximation methods are described, that do not break any-time behavior, while causing the intermediate result of an any-time controller to come closer to the final (full calculation) result in a shorter time. The exact influence of the approximation methods should be further researched.
@MastersThesis{edi-msc,
author = {Edi Shmukler},
title = {Anytime Fuzzy Control},
school = {{B}ar {I}lan {U}niversity},
year = {2006},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
note = {},
abstract = { Fuzzy logic has been successfully applied in various fields.
However, as fuzzy controllers increase in size and complexity, the
number of control rules increases exponentially and real-time
behavior becomes more difficult. This thesis introduces an
any-time fuzzy controller. Much work has been done to optimize and
speed up a controlling process, however none of the existing
solutions provides an any-time behavior. This study first
introduces several constraints that should be satisfied in order
to guarantee an any-time behavior. These constraints are related
to aggregation and defuzzification phases of fuzzy control.
Popular aggregation (max-min, sum-product) and defuzzification methods (mean-of-maxima (MOM) and center-of-gravity (COG)) are first shown to satisfy these
constraints, and then three linearization methods are presented.
Linearization methods are used to reorder fuzzy rule-bases such
that a reordered rule-base would result in any-time behavior.
Finally, several approximation methods are described, that do not
break any-time behavior, while causing the intermediate result of
an any-time controller to come closer to the final (full
calculation) result in a shorter time. The exact influence of the approximation methods should be further researched.},
wwwnote = {},
OPTannote = {}
}
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