Paper Info
Title
Harnessing Fuzziness of the Pragmatic Rule-Design Without IF-THEN Rules.
Abstract
In this study, we display preliminary results for harnessing fuzziness of yet-another fuzzy rule-bases. They are based on the pragmatic rule-design (PRD), which has been proposed by the authors. The PRD is novel since a pragmatic rule is not an "IF-THEN" rule nor an artificial neural network, and does not represent a stimulus-response relation. A pragmatic rule is a vector of relative characteristics of effective responses in itself. In the original PRD, the fuzziness in discretizing a system state is too surplus. Restricting such fuzziness may improve the performance of the rule-base, therefore a modification of the original PRD is proposed. Some PRD variants based on that modification are developed and evaluated through their applications to elevator operation problems.
Year
DOI
Venue
2017
10.3233/978-1-61499-828-0-54
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
rule-base,genetics-based machine learning,elevator operation,simulation
Computer science,Artificial intelligence
Conference
Volume
ISSN
Citations 
299
0922-6389
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Tsutomu Inamoto102.37
Yoshinobu Higami214027.24