A learning Fuzzy Petri net model

Fuzzy Petri net (FPN) is a powerful modeling tool for the construction of knowledge systems. In this paper, we propose a new learning model tool—learning fuzzy Petri net (LFPN). In contrast with the existing FPN, there are three extensions in the new model: (i) the place can possess different tokens which represent different propositions; (ii) these propositions have different degrees of truth toward different transitions; and (iii) the truth degree of the proposition can be learned by adjusting the arc's weight function. The LFPN model obtains the capability of learning fuzzy production rules through truth degree updating. The LFPN learning algorithm which introduces network learning method into Petri net update is proposed and the convergence of algorithm is analyzed. Finally, for the purpose of certification of the effectiveness of the proposed model, LFPN is used to model the Web service discovery. The result of the simulation shows that the proposed LFPN is useful and effective. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.