A Hybrid Fuzzy Petri Nets and Neural Networks Framework for Modeling Critical Infrastructure Systems

Critical Infrastructure Systems (CISs) play an essential role in our life, when disasters, attacks, failures happen, such complex systems are expected to be reliable and safety, even react to undesirable accidents. Modeling CISs and developing methods to analyze their safety and dependability is of utmost importance. CIS modeling formalisms must be able to describing both discrete and continuous quantities, a hybrid system modelling approach is natural. In this work, CISs are modeled from two aspects: logic and continuous; Adaptive fuzzy Petri nets (AFPN) and neural networks are combined in our framework, where AFPN is adopted to model the logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples are illustrated to show the effectiveness of the proposed approach.