Learning Algorithm of Fuzzy Petri Net Based on Result-Feedback
基于结果反馈的模糊Petri网学习算法

With regard to the complex structure of Fuzzy Petri Net, this paper improved the hierarchical algorithm of Fuzzy Petri Net without increasing the virtual place and virtual transition, thereby simplifying the learing and training of Fuzzy Petri Net. To speed convergence, this paper proposed a new algorithm for the learning of Fuzzy Petri Net based on the results feedback, namely FBFPN. Firstly, this algorithm layered the pure net hierarchically and established the approximate continuous function of the transition firing, then adjusted the weight, the threshold and the credibility ,finally adjusted the input vector to minimize the error function. Simulation results showed that this algorithm has stronger generalization ability and higher learning efficiency.