Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Validating neural network-based online adaptive systems: a case study
Software Quality Control
Novelty detection for a neural network-based online adaptive system
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Developing artificial agents worthy of trust: "Would you buy a used car from this artificial agent?"
Ethics and Information Technology
An approach to v&v of embedded adaptive systems
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
| Hi-index | 0.00 |
Online adaptation is a powerful means to handle unexpected slow or catastrophic changes of the system's behavior (e.g., a stuck or broken rudder of an aircraft). Therefore, adaptation is one way for realizing a self-healing system. Substantial research and development has been made to use neural networks (NN) for such tasks (e.g., integrated in various unmanned helicopters and test-flown on a modified F-15 aircraft). Despite the advantages of adaptive neural network based systems, the lack of methods to perform certification, verification, and validation (V&V) of such systems severely restricts their applicability.In this paper, we report on ongoing work to develop V&V techniques and processes for NN-based safety-critical control systems, in our case an aircraft flight control system. Although the project ultimately aims at V&V of online adaptive systems, this paper focuses on the first part of this project dealing with so-called pre-trained neural networks (PTNN). V&V techniques developed here are important pre-requisites for handling the online adaptive case. In particular, we describe highlights of a process guide which has been developed within this project and discuss important V&V issues which need to be addressed during certification.