Q-rung Orthopair Fuzzy Petri Nets for Knowledge Representation and Reasoning

This paper investigates a novel fuzzy Petri nets (FPNs) method based on q-rung orthopair sets (q-ROFSs) to provide an efficient solution uncertain knowledge representation and reasoning. It not only improves FPN’s flexibility in parameter reasoning algorithms but also addresses the challenging problem that most FPNs cannot implement backward reasoning, which is common task reversely inferring condition statuses according consequences. Specifically, we first propose (q-ROFPNs) by integrating q-ROFSs with FPNs. achieves intuitive evaluation of hesitancy information flexible adjustment ranges. And algorithm ordered weighted averaging-weighted average (OWAWA) operator developed accomplish forward driven q-ROFPNs, can flexibly balance proposition weights its position weights. Building upon further reversed (q-ROFRPNs) for task, decomposition q-ROFRPNs designed reducing inference complexity, (OWBR) provided suitable different environments. In addition, ensure accuracy rationality results, acquisition power (PA) eliminate negative impact outlier assessments. A simulation experiment fault diagnosis air conditioning system demonstrates proposed achieve more reliable than state-of-the-art methods.