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):

Report Date:

2016-05-31

Pagination or Media Count:

9.0

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.

Subject Categories:

  • Cybernetics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE