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

Period of Performance: 01/01/2004 - 12/31/2004

$100K

Phase 1 STTR

Recipient Firm

Prologic, Inc.
1000 Technology Drive Suite 3140
Fairmont, WV 26554
Principal Investigator
Firm POC

Research Institution

Institute for Scientific Research, Inc.
320 Adams Street, PO Box 2720
Fairmont, WV 26555
Institution POC

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.