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FUZZY BELIEF NETS Information Technology Lab, General Electric Corporate Research and Development
 

Summary: 1
FUZZY BELIEF NETS
KAI GOEBEL
Information Technology Lab, General Electric Corporate Research and Development
K1-5C4A, One Research Circle ,Niskayuna, NY 12309, USA
ALICE AGOGINO
Department of Mechanical Engineering
University of California at Berkeley, Berkeley, CA 94720-1740, USA
Received (April 6, 1998)
Revised (January 20, 1999)
This paper introduces fuzzy belief nets (FBN). The ability to invert arcs between nodes is key to
solving belief nets. The inversion is accomplished by defining closeness measures which allow
diagnostic reasoning from observed symptoms to cause of failures. The closeness measures are
motivated by a Lukasiewicz operator which takes into account the distance from an observed
symptom set to the modeled symptom set for all failure combinations. Hypothesized failures are
then ranked according to maximum closeness measure and minimum cover, i.e., number of faults.
Within the realm of fuzzy logic we show the graphical representation and solution of fuzzy belief
nets.
Keywords: Diagnosis, Reasoning under Uncertainty, Causal Diagrams, Belief Nets
1. Introduction

  

Source: Agogino, Alice M. - Department of Mechanical Engineering, University of California at Berkeley

 

Collections: Engineering