A Visualization Algorithm for Defeasible Logic Rule Bases over RDF Data

2007 ◽  
pp. 367-369
Author(s):  
Efstratios Kontopoulos ◽  
Nick Bassiliades ◽  
Grigoris Antoniou
Keyword(s):  
Defeasible Logic ◽  
Rdf Data ◽  
Rule Bases ◽  
Logic Rule

Deploying defeasible logic rule bases for the semantic web

2008 ◽  
Vol 66 (1) ◽  
pp. 116-146 ◽  
Author(s):  
Efstratios Kontopoulos ◽  
Nick Bassiliades ◽  
Grigoris Antoniou
Keyword(s):  
Semantic Web ◽  
Defeasible Logic ◽  
Rule Bases ◽  
Logic Rule

VISUAL MODELING OF DEFEASIBLE LOGIC RULES WITH DR-VisMo

2008 ◽  
Vol 17 (05) ◽  
pp. 903-924 ◽  
Author(s):  
EFSTRATIOS KONTOPOULOS ◽  
NICK BASSILIADES ◽  
GRIGORIS ANTONIOU ◽  
ANNA SERIDOU
Keyword(s):  
Semantic Web ◽  
Directed Graphs ◽  
Rule Base ◽  
Full Potential ◽  
Visualization System ◽  
Defeasible Logic ◽  
Conflicting Information ◽  
Rule Bases ◽  
Available Information ◽  
Logic Rule

The standardization of the Semantic Web has reached as far as ontologies and ontology languages. However, in order for the full potential of the Semantic Web to be achieved, the ability of reasoning over the available information is also essential. Rules can assist in this affair and various logics have been proposed for the Semantic Web domain. One of them is defeasible reasoning that deals with incomplete and conflicting information. However, despite its solid mathematical notation, it may be confusing to end users. To confront this downside, we proposed a representation schema for defeasible logic rule bases, which is based on directed graphs that feature distinct node and connection types. This paper presents DR-VisMo, a defeasible logic rule base editor and visualization system that implements this representation approach. The system also features a stratification algorithm for visualizing rule bases that deals with decisions, regarding the arrangement of the various elements in the graph. DR-VisMo is implemented as part of VDR-DEVICE, an environment for modeling and deploying defeasible logic rule bases on top of RDF ontologies.

scholarly journals A Novel Self-Tuning Fuzzy Logic-Based PID Controllers for Two-Axis Gimbal Stabilization in a Missile Seeker

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
S. Senthil Kumar ◽  
G. Anitha
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Control Method ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Self Tuning ◽  
Essential Function ◽  
Tracking Device ◽  
Rule Bases ◽  
Logic Rule

Tracking a target is an essential function of a seeker for missiles. The target tracking mechanism of a seeker consists of gimbals, mounted with gyroscopes, and an antenna or some other energy receiving devices such as radar, infrared (IR), or laser. Stabilization of such a gimbal is necessary for any guided missile to maintain the tracking device always pointing towards the target. For the stabilization of the gimbal system, several control methods have been employed for making the gimbal to follow an input rate command by eliminating all the gimbal disturbances. Here, a new self-tuning fuzzy logic-based proportional, integral, derivative (PID) controller is introduced for the stabilization of a two-axis gimbal for a manoeuvring guided missile. The proposed control method involves tuning the gains of the PID controller based on the fuzzy logic rule bases considering the missile body rotation. The performance of the stabilization loops has been verified through MATLAB simulations for fuzzy logic-based PID controller compared with the conventional PID controller. The simulation results show the response of the gimbal system with stabilization loops met the control requirements with fuzzy PID controllers but not with conventional PID controllers.

A fine tuning method for fuzzy logic rule bases

1994 ◽  
Vol 67 (2) ◽  
pp. 147-161 ◽  
Author(s):  
H.B. Gürocak ◽  
A. de Sam Lazaro
Keyword(s):  
Fuzzy Logic ◽  
Fine Tuning ◽  
Tuning Method ◽  
Rule Bases ◽  
Logic Rule

An Unsupervised Instance Matcher for Schema-Free RDF Data

2015 ◽  
Author(s):  
Mayank Kejriwal ◽  
Daniel P. Miranker
Keyword(s):  
Rdf Data
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