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

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNA04AA20C
Agency Tracking Number: 030126
Amount: $99,963.00
Phase: Phase I
Program: STTR
Awards Year: 2004
Solicitation Year: 2003
Solicitation Topic Code: T1.01
Solicitation Number: N/A
Small Business Information
1000 Technology Drive, Fairmont, WV, 26554-8824
DUNS: 941134876
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Marjorie Darrah
 Principal Investigator
 () -
 mdarrah@isr.us
Business Contact
 Ken Snyder
Title: Business Official
Phone: (304) 363-1157
Email: ksnyder@prologic-inc.com
Research Institution
 Institue for Scientific Research, Inc.
 Not E Available
 320 Adams Street, PO Box 2720
Fairmont, WV, 26555
 (304) 368-9300
 Nonprofit college or university
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. *

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