Authors:
Antonio Sala
;
Alicia Esparza
;
Carlos Ariño
and
Jose V. Roig
Affiliation:
Univ. Politécnica de Valencia, Spain
Keyword(s):
Fault detection and diagnosis, fuzzy mathematical programming, approximate reasoning, optimisation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Expert Systems
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
This paper discusses how to encode fuzzy knowledge bases for diagnostic tasks (i.e., list of symptoms produced by each fault, in linguistic terms described by fuzzy sets) as constrained optimisation problems. The proposed setting allows more flexibility than some fuzzy-logic inference rulebases in the specification of the diagnostic rules in a transparent, user-understandable way (in a first approximation, rules map to zeros and ones in a matrix), using widely-known techniques such as linear and quadratic programming.