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