Multi-state systems reliability with composite importance measures of fuzzy petri nets

Abstract

A fuzzy petri nets method for modelling and analysing the reliability of a multi-state software system is proposed here. This new method is based on fuzzy parameter of the FPN, which depends on the state probability which is learned by the enhanced fuzzy reasoning algorithm. First, the multi-state system model of fuzzy petri nets was proposed. Second, fuzzy petri nets adaptive learning algorithms were proposed, and the state probability is drawn to the optimal value. Then the reliability of the multi-state system was determined. Finally, with respect to the different Type 1 importance measures, according to the state probability, multi-state system, the reliability of the state with fuzzy petri nets (FPN) composite importance measures (CIM) was estimated and the comparison results table between the CIM and the FPN CIM was produced. From the experiment, multi-state system reliability with composite importance measures of fuzzy petri nets in this paper shows that the variances are consistently low, and the measures can be considered to be robust. This makes the system more reliable and accuracy from the user's perspective, the method becomes more efficient.