Optimizing Parameters of Fuzzy Petri Net Based on Artificial Immune Algorithm

摘要

Aiming at knowledge reasoning ability of fuzzy petri net depending on the parameter and the parameters usually obtained by specialist, an algorism based on artificial immune algorism for obtaining the optimum parameters was proposed. Firstly, the fuzzy Petri net and generating rules were defined and described, and then the coding method of antibody, Affinity evaluation function and Simulated Annealing immune selection operator are designed to improve the classic artificial immune algorism. The specific algorism based on this improved artificial algorism was defined. The simulation experiment shows the method in this paper can accurately realize the parameters optimizing and has the litter square error, compared with the other methods, our method has the quick global convergence rate, optimizing ability and strong Versatility.