Encoding fuzzy possibilistic diagnostics as a constrained optimization problem

This paper discusses a knowledge-base encoding methodology for diagnostic tasks, transforming such knowledge into constrained optimization problems. The methodology is based on a reinterpretation of the consistent causal reasoning paradigm [D. Dubois, H. Prade, Fuzzy relation equations and causal re...

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Bibliographic Details
Published in:Information sciences Vol. 178; no. 22; pp. 4246 - 4263
Main Author: Sala, Antonio
Format: Journal Article
Language:English
Published: Elsevier Inc 15.11.2008
Subjects:
Approximate reasoning
Fault detection and diagnosis
Optimization
Possibilistic reasoning
Approximate reasoning
Possibilistic reasoning
Fault detection and diagnosis
Optimization
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:This paper discusses a knowledge-base encoding methodology for diagnostic tasks, transforming such knowledge into constrained optimization problems. The methodology is based on a reinterpretation of the consistent causal reasoning paradigm [D. Dubois, H. Prade, Fuzzy relation equations and causal reasoning, Fuzzy Sets and Systems 45 (2) (1995) 119–134] as an equivalent problem of feasibility subject to equality and inequality constraints (in the binary case). Then, it is extended to the fuzzy case. Preferences under uncertain knowledge are incorporated by transforming the feasibility problem into an optimization one, which may be interpreted in possibilistic terms. The problem is solved by efficient, widely-known, linear and quadratic programming tools, which are able to cope with large-scale problems. Examples illustrating some of the concepts and possibilities of the proposed procedure, as well as a summary comparison with other approaches are also discussed.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2008.07.017