Abstract
Online Adaptive Systems cannot be certified using traditional testing and proving methods, because these methods rely on assumptions that do not hold for such systems. In this paper we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform the verification of these systems. In addition to the framework, we present some preliminary results on concrete neural network models.
| Original language | English (US) |
|---|---|
| Article number | STTCT06 |
| Pages (from-to) | 4857-4866 |
| Number of pages | 10 |
| Journal | Proceedings of the Hawaii International Conference on System Sciences |
| Volume | 37 |
| State | Published - 2004 |
| Event | Proceedings of the Hawaii International Conference on System Sciences - Big Island, HI., United States Duration: Jan 5 2004 → Jan 8 2004 |
All Science Journal Classification (ASJC) codes
- General Computer Science
Keywords
- Adaptive Control
- Formal Methods
- MLP neural networks
- Neural Networks
- On-Line Learning
- RBF neural networks
- Radial Basis Functions
- Refinement Calculi
- Verification and Validation