Please use this identifier to cite or link to this item: https://doi.org/10.1109/49.257935
Title: Probabilistic approach to fault diagnosis in linear lightwave networks
Authors: Deng, Robert H. 
Lazar, Aurel A. 
Wang, Weiguo 
Issue Date: Dec-1993
Citation: Deng, Robert H., Lazar, Aurel A., Wang, Weiguo (1993-12). Probabilistic approach to fault diagnosis in linear lightwave networks. IEEE Journal on Selected Areas in Communications 11 (9) : 1438-1448. ScholarBank@NUS Repository. https://doi.org/10.1109/49.257935
Abstract: The application of probabilistic reasoning to fault diagnosis in Linear Lightwave Networks (LLN's) is investigated. The LNN inference model is represented by a Bayesian network (or casual network). An inference algorithm is proposed that is capable of conducting fault diagnosis (inference) with incomplete evidence and on an interactive basis. Two belief updating algorithms are presented which are used by the inference algorithm for performing fault diagnosis. The first belief updating algorithm is a simplified version of the one proposed by Pearl for singly conceited inference models. The second belief updating algorithm applies to multiply connected inference models and is more general than the first. We also introduce a t-fault diagnosis system and an adaptive diagnosis system to further reduce the computational complexity of the fault diagnosis process.
Source Title: IEEE Journal on Selected Areas in Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/62654
ISSN: 07338716
DOI: 10.1109/49.257935
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