A visual explanation system for explaining fuzzy reasoning results by fuzzy rule-based classifiers

2008 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Yutaka Kaisho ◽  
Yusuke Nojima
Keyword(s):  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Rule Based ◽  
Explanation System

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

scholarly journals Using the Choquet Integral in the Fuzzy Reasoning Method of Fuzzy Rule-Based Classification Systems

Axioms ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 208-223 ◽  
Author(s):  
Edurne Barrenechea ◽  
Humberto Bustince ◽  
Javier Fernandez ◽  
Daniel Paternain ◽  
José Sanz
Keyword(s):  
Choquet Integral ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Classification Systems ◽  
Rule Based

FUZZY PETRI NETS FOR MODELING RULE-BASED REASONING

1998 ◽  
Vol 07 (04) ◽  
pp. 463-485 ◽  
Author(s):  
JONATHAN LEE ◽  
KEVIN F. R. LIU ◽  
WEILING CHIANG
Keyword(s):  
Petri Nets ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Rules ◽  
Rule Based ◽  
Imprecise Information ◽  
Fuzzy Petri Nets ◽  
Reasoning Algorithm ◽  
Modeling Rule

In this paper, a fuzzy Petri nets for modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning: fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts, can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions, inference, aggregation, duplication and aggregation-duplication transitions, are introduced to meet the mechanism of fuzzy rule-based reasoning. A reasoning algorithm based on fuzzy Petri nets is also presented to improve the efficiency of fuzzy rule-based reasoning. The reasoning algorithm is consistent with not only the rule-based reasoning but also the execution of Petri nets.

Face Recognition by Fuzzy Rule-Based Local Binary Pattern

2014 ◽  
Vol 8 (3) ◽  
pp. 31-34
Author(s):  
O. Rama Devi ◽  
◽  
L. S. S. Reddy ◽  
E. V. Prasad ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Fuzzy Rule ◽  
Rule Based
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