The Strategies of Optimizing Fuzzy Petri Nets by Using an Improved Genetic Algorithm
Abstract: It is very important
for constructing a FPN (fuzzy petri net) to accurately find out all parameters
of fuzzy production rules. In this paper, an improved genetic algorithm is
introduced into the process of exploring the optimal parameters of a modified
FPN. Realization of the algorithm does not depend on experiential data and
requirements for the initial input of the FPN are not stringent. Simulation
experiment shows that the parameters trained by the above algorithm are highly
accurate and the FPN model constructed by these parameters possesses strong
generalizing capability and self-adjusting purpose.
Author: Li Yang, Yue Xiao-bo
Journal Code: jptkomputergg160111
