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 : https://apps.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