A feedback Adaptive fuzzy Petri net model for context reasoning

As an improved model of fuzzy Petri net, adaptive Petri net (AFPN) has got the learning ability from neural network. But AFPN still depends on offline training data, while actual environment is so complex, vague and changeful that AFPN seems slightly inadequate. This paper proposes an approach based on fuzzy logic and feedback theory to improve AFPN. The approach introduces feedback mechanisms into AFPN to enhance the adaptive ability in dynamic environment. In addition, the approach embeds fuzzy logic theory into the representation of context information. Thus, the uncertain context information management is more conformable with person´s sense. The approach is also able to learn the parameters of membership function by using the back propagation algorithm of neural network. At the end of the paper, an experiment is designed to demonstrate that the approach is feasible and effective in fuzzy reasoning.