Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

Since knowledge in expert system is vague and modified frequently, expert systems are fuzzy and dynamic systems. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. Aiming at this object, a generalized fuzzy Petri net model is proposed in this paper, it is called adaptive fuzzy Petri net (AFPN). AFPN not only takes the descriptive advantages of fuzzy Petri net, but also has learning ability like neural network. Just as other fuzzy Petri net (FPN) models, AFPN can be used for knowledge representation and reasoning, but AFPN has one important advantage: it is suitable for dynamic knowledge, i.e., the weights of AFPN are ajustable. Based on AFPN transition firing rule, a modified back propagation learning algorithm is developed to assure the convergence of the weights.