A Formal Method for Verification and Validation of Neural Network High Assurance Systems

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,963.00
Award Year:
2004
Program:
STTR
Phase:
Phase I
Contract:
NNA04AA20C
Award Id:
72057
Agency Tracking Number:
030126
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1000 Technology Drive, Fairmont, WV, 26554
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
941134876
Principal Investigator:
Marjorie Darrah
Principal Investigator
() -
mdarrah@isr.com
Business Contact:
Ken Snyder
Business Official
(304) 363-1157
ksnyder@prologic-inc.com
Research Institution:
Institute for Scientific Research, Inc.
Paul E Parker, III
320 Adams Street, PO Box 2720
Fairmont, WV, 26555
(304) 368-9300
Domestic nonprofit research organization
Abstract
Our proposed innovation is to develop neural network (NN) rule extraction technology to a level where it can be incorporated into a software tool, we are calling NNRules, which captures a trained neural network?s decision logic and uses it as a basis for verification and validation (V&V) of the neural network. This formalism has never been attempted. The significance of the NNRules innovation is that: ? The National Aeronautics and Space Administration, the Department of Defense, the Department of Energy, and the Federal Aviation Administration are currently researching the potential of neural networks in mission- and safety-critical systems. ? High assurance neural network applications require rigorous verification and validation techniques. ? The adaptive and ?black box? characteristics of neural networks make verification and validation of neural networks practically intractable. ? Rule-based systems have a more visible, and potentially human readable, decision logic that supports a robust set of verification techniques. ? Neural network rule extraction research has developed algorithms that translate a neural network into an equivalent set of rules. NNRules embeds this technology in a generally usable tool that will dramatically increase the ability to V&V high assurance neural networks.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government