Accession Number : AD1024898
Title : Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling
Descriptive Note : Technical Report,15 Apr 2014,14 Jan 2015
Corporate Author : West Virginia University Research Corporation Morgantown United States
Personal Author(s) : Bell,Suzanne
Full Text : http://www.dtic.mil/dtic/tr/fulltext/u2/1024898.pdf
Report Date : 31 May 2016
Pagination or Media Count : 9
Abstract : This was a short-term proof-of-concept project with the goal of demonstrating the feasibility of, and lay the theoretical foundations for, integration of predictive neural networks into Bayesian networks as a means of generating probability distribution functions and likelihood tables. The challenges were two-fold: first, developing a way to convert XY data output from an instrument to a probability density functionusing a neural network and secondly, fusing this and other types of sensor output into a single probabilistic evaluation of multiple sensor outputs. Ultimately, this would be useful in application such as networked sensor arrays such as might be deployed to detect chemical agentsin a subway system for example.
Descriptors : artificial neural networks , bayseian networks , probability density functions , data fusion
Subject Categories : Cybernetics
Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE
