Modeling Self-Adaptive Software Systems with Learning Petri Nets

Zuohua Ding, Yuan Zhou, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment; 2) the components in the model can collaborate to make adaption decisions while the system is running; and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system.

Original languageEnglish (US)
Article number7115165
Pages (from-to)483-498
Number of pages16
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume46
Issue number4
DOIs
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Adaptive Petri net (APN)
  • Requirement modeling
  • adaptive software system
  • neural network

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