Correspondence between causality diagram and neural networks

The problem of obtaining a correspondence between causality diagram (CD) and neural networks was studied. A method of obtaining a direct correspondence between the parameters of a causality diagram and the parameters of an associated neural network has been presented. The training capabilities of a...

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Bibliographic Details
Published in2005 IEEE International Conference on Granular Computing Vol. 1; pp. 185 - 188 Vol. 1
Main Authors Liang Xinyuan, Shi Qingxi, Zhang Qin
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Causality Diagram
Conditional probability matrices
Correspondence
Couplings
Educational technology
Electronic mail
Function approximation
Geographic Information Systems
Inference mechanisms
Mathematical model
Multilayer perceptrons
Neural networks
Uncertainty
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Summary:The problem of obtaining a correspondence between causality diagram (CD) and neural networks was studied. A method of obtaining a direct correspondence between the parameters of a causality diagram and the parameters of an associated neural network has been presented. The training capabilities of a neural network were used to determine the conditional probability matrix elements required by the causality diagram. It is shown how such a correspondence is established by obtaining a mathematical function which relates the parameters of the two models. It shows the validity of the method by deriving the parameters to be used in a causality diagram constructed to combine GIS data for assessing the risk of desertification of burned forest areas in the Northeast China.
ISBN:0780390172
9780780390171
DOI:10.1109/GRC.2005.1547263