Towards the verification and validation of online learning systems: General framework and applications

Ali Mili, Guang Jie Jiang, Bojan Cukic, Yan Liu, Rahma Ben Ayed

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

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 languageEnglish (US)
Article numberSTTCT06
Pages (from-to)4857-4866
Number of pages10
JournalProceedings of the Hawaii International Conference on System Sciences
Volume37
StatePublished - Dec 1 2004
EventProceedings of the Hawaii International Conference on System Sciences - Big Island, HI., United States
Duration: Jan 5 2004Jan 8 2004

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Keywords

  • Adaptive Control
  • Formal Methods
  • MLP neural networks
  • Neural Networks
  • On-Line Learning
  • RBF neural networks
  • Radial Basis Functions
  • Refinement Calculi
  • Verification and Validation

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