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Anytime Fuzzy Control

Edi Shmukler. Anytime Fuzzy Control. Master's Thesis, Bar Ilan University,2006.

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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.

Additional Information

BibTeX

@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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