NDLI: Correspondence between causality diagram and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Liang Xinyuan Shi Qingxi Zhang Qin
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci., Chongqing Univ., China (Liang Xinyuan; Shi Qingxi)
Abstract	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.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	185
Ending Page	188
File Size	1080853
Page Count	4
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547263
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geographic Information Systems Uncertainty Educational technology Electronic mail Function approximation Couplings Inference mechanisms Causality Diagram Neural networks Multilayer perceptrons Correspondence Mathematical model Conditional probability matrices
Content Type	Text
Resource Type	Article

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