Uncertainty modelling of dynamically reconfigurable systems based on rewriting stochastic reward nets with z-fuzzy parameters

Abstract

This paper presents the descriptive compositional approach for uncertainty modelling and performance evaluation of dynamic reconfigurable discrete event systems (ReDES) using rewriting stochastic reward nets (ReSRN) with Z-fuzzy parameters (FReSRN) that can modify in run-time their own structure by the rewriting of the rules. The expected Z-fuzzy values of the transition and rewriting rule firing rates are calculated based on credibility theory, the FReSRN model is degenerated to a conventional ReSRN model. A case study for performance modelling and analysis of particular ReDES is given in order to show the effectiveness of the proposed method.