A Tool for Verification and Validation of Neural Network Based Adaptive
Controllers for High Assurance Systems
Proc. HASE 2004, 2004
Abstract
High reliability of mission- and safety-critical software
systems has been identified by NASA as a high-priority technology challenge.
We present an approach for the performance analysis of a neural
network (NN) in an advanced adaptive control system.
This problem is important in the context of safety-critical applications that
require certification, such as flight software in aircraft.
We have developed a tool to measure the performance of the NN
during operation by calculating a confidence interval (error bar)
around the NN's output.
Our tool can be used during pre-deployment verification
as well as monitoring the network performance during operation.
The tool has been implemented in Simulink and simulation results on
a F-15 aircraft are presented.