Dynamic representation of fuzzy knowledge based on fuzzy petri net and genetic-particle swarm optimization

作者：

Highlights：

• The model of dynamic representation of fuzzy knowledge is proposed.

• The model has both the features of a fuzzy Petri net and the learning ability of evolutionary algorithms.

• The improved Genetic Particle Swarm Optimization (GPSO) learning algorithm can solve fuzzy knowledge representation parameters efficiently.

• The validity of the method has been demonstrated by using it in the fault diagnoses of launch vehicle.

摘要

•The model of dynamic representation of fuzzy knowledge is proposed.•The model has both the features of a fuzzy Petri net and the learning ability of evolutionary algorithms.•The improved Genetic Particle Swarm Optimization (GPSO) learning algorithm can solve fuzzy knowledge representation parameters efficiently.•The validity of the method has been demonstrated by using it in the fault diagnoses of launch vehicle.

论文关键词：Fuzzy knowledge,Petri nets,Knowledge representation,Learning algorithms,Particle swarm optimization

论文评审过程：Available online 5 September 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.08.034